Reagents and methods for use in cancer diagnosis, classification and therapy

ABSTRACT

Methods and reagents for classifying tumors and for identifying new tumor classes and subclasses. Methods for correlating tumor class or subclass with therapeutic regimen or outcome, for identifying appropriate (new or known) therapies for particular classes or subclasses, and for predicting outcomes based on class or subclass. New therapeutic agents and methods for the treatment of cancer.

PRIORITY INFORMATION

This application is a divisional application of U.S. Ser. No. 12/879,356filed Sep. 10, 2010, which is a divisional application of U.S. Ser. No.12/013,739 filed Jan. 14, 2008, which is a divisional application ofU.S. Ser. No. 11/061,067 filed Feb. 18, 2005, which is acontinuation-in-part of U.S. Ser. No. 10/915,059 filed Aug. 10, 2004which claimed priority to U.S. Ser. No. 60/494,334 filed Aug. 11, 2003and U.S. Ser. No. 60/570,206 filed May 12, 2004. The entire contents ofeach of these applications are hereby incorporated by reference.

SEQUENCE LISTING

In accordance with 37 CFR 1.52(e)(5), a Sequence Listing in the form ofa text file (entitled “Sequence Listing.txt,” created on Sep. 7, 2010,and 93 kilobytes) is incorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION

A major challenge of cancer treatment is the selection of therapeuticregimens that maximize efficacy and minimize toxicity for a givenpatient. A related challenge lies in the attempt to provide accuratediagnostic, prognostic and predictive information. At present, tumorsare generally classified under the tumor-node-metastasis (TNM) system.This system, which uses the size of the tumor, the presence or absenceof tumor in regional lymph nodes, and the presence or absence of distantmetastases, to assign a stage to the tumor is described in the AJCCCancer Staging Manual, Lippincott, 5th ed., pp. 171-180 (1997). Theassigned stage is used as a basis for selection of appropriate therapyand for prognostic purposes. In addition to the TNM parameters,morphologic appearance is used to further classify tumors into tumortypes and thereby aid in selection of appropriate therapy. However, thisapproach has serious limitations. Tumors with similar histopathologicappearance can exhibit significant variability in terms of clinicalcourse and response to therapy. For example, some tumors are rapidlyprogressive while others are not. Some tumors respond readily tohormonal therapy or chemotherapy while others are resistant.

Assays for cell surface markers, e.g., using immunohistochemistry, haveprovided means for dividing certain tumor types into subclasses. Forexample, one factor considered in prognosis and treatment decisions forbreast cancer is the presence or absence of the estrogen receptor (ER)in tumor samples. ER-positive breast cancers typically respond much morereadily to hormonal therapies such as tamoxifen, which acts as ananti-estrogen in breast tissue, than ER-negative tumors. Though useful,these analyses only in part predict the clinical behavior of breasttumors. There is phenotypic diversity present in cancers that currentdiagnostic tools fail to detect. As a consequence, there is still muchcontroversy over how to stratify patients amongst potential treatmentsin order to optimize outcome (e.g., for breast cancer see “NIH ConsensusDevelopment Conference Statement: Adjuvant Therapy for Breast Cancer,Nov. 1-3, 2000”, J. Nat. Cancer Inst. Monographs, 30:5-15, 2001 and DiLeo et al., Int. J. Clin. Oncol. 7:245-253, 2002).

There clearly exists a need for improved methods and reagents forclassifying tumors. Once these methods and reagents are available,clinical studies can be performed that will allow the identification ofclasses or subclasses of patients having different prognosis and/orresponses to therapy. Such prognostic tools will allow more rationallybased choices governing the aggressiveness of therapeutic interventions;such predictive tools will also be useful for directing patients intoappropriate treatment protocols.

SUMMARY OF THE INVENTION

The invention encompasses the realization that particular panels oftumor sample binding agents (“interaction partners”) can be used toprovide new insights into the biology of cancer. Among other things, thepresent invention provides methods and reagents for classifying tumorsand for identifying new tumor classes and subclasses. The inventionfurther provides methods for correlating tumor class or subclass withtherapeutic regimen or outcome, for identifying appropriate (new orknown) therapies for particular classes or subclasses, and forpredicting outcomes based on class or subclass. The invention furtherprovides new therapeutic agents and methods for the treatment of cancer.

For example, the present invention provides methods for identifyingsuitable panels of interaction partners (e.g., without limitationantibodies) whose binding is correlated with any of a variety ofdesirable aspects such as tumor class or subclass, tumor source (e.g.,primary tumor versus metastases), likely prognosis, responsiveness totherapy, etc. Specifically, collections of interaction partners areselected and their activity in binding to a variety of different tumors,normal tissues and/or cell lines is assessed. Data are collected formultiple interaction partners to multiple samples and correlations withinteresting or desirable features are assessed. As described herein, thedetection of individual interaction partners or panels thereof that binddifferentially with different tumors provides new methods of use incancer prognosis and treatment selection. In addition, these interactionpartners provide new therapies for treating cancer.

As described in further detail below, the invention employs methods forgrouping interaction partners within a panel into subsets by determiningtheir binding patterns across a collection of samples obtained fromdifferent tumor tissues, normal tissues and/or cell lines. The inventionalso groups the tumor samples into classes or subclasses based onsimilarities in their binding to a panel of interaction partners. Thistwo-dimensional grouping approach permits the association of particularclasses of tumors with particular subsets of interaction partners that,for example, show relatively high binding to tumors within that class.Correlation with clinical information indicates that the tumor classeshave clinical significance in terms of prognosis or response tochemotherapy.

BRIEF DESCRIPTION OF APPENDICES A-F

This patent application refers to material comprising tables and datapresented as appendices.

Appendix A is a table that lists the antibodies included in the breast,lung and/or colon panels that are discussed in the Examples. The tableincludes the antibody ID, parent gene name, NCBI LocusLink ID, UniGeneID, known aliases for the parent gene, peptides that were used inpreparing antibodies, antibody titer and a link to any relevant IHCimages of Appendix B. Using the parent gene name, NCBI LocusLink ID,UniGene ID, and/or known aliases for the parent gene, a skilled personcan readily obtain the nucleotide (and corresponding amino acid)sequences for each and every one of the parent genes that are listed inAppendix A from a public database (e.g., GenBank, Swiss-Prot or anyfuture derivative of these). The nucleotide and corresponding amino acidsequences for each and every one of the parent genes that are listed inAppendix A are hereby incorporated by reference from these publicdatabases. Antibodies with AGI IDs that begin with s5 or s6 wereobtained from commercial sources as indicated. The third and fourthcolumns of Appendix A indicate whether the antibodies of the breastcancer classification panel were identified by staining with the Russianbreast cohort (Example 2) and/or the HH breast cohort (Example 3). Thefifth and sixth columns indicate whether the antibodies of the lungcancer classification panel were identified by staining with the Russianlung cohort (Example 4) and/or the HH lung cohort (Example 5). Theseventh column indicates the antibodies of the colon cancerclassification panel. These were all identified by staining with theRussian colon cohort (Example 6).

Appendix B includes breast IHC images, colon IHC images and lung IHCimages. The IHC images of Appendix B are referenced in the right handcolumn of Appendix A. An actual copy of Appendix B is not included withthis continuation-in-part but can be found in parent case U.S. Ser. No.10/915,059 filed Aug. 10, 2004 (published as US 2005/0112662 on May 26,2005), the entire contents of which are hereby incorporated byreference.

Appendix C is a table that lists exemplary antibodies whose bindingpatterns have been shown to correlate with tumor prognosis in breastcancer patients. The results are grouped into four categories that havebeen clinically recognized to be of significance: all patients, ER+patients, ER− patients, and ER+/lymph node metastases negative(ER+/node−) patients. Scoring methods 1-3 use the following schemes:method 1 (0=negative; 1=weak; 2=strong); method 2 (0=negative; 1=weak orstrong); and method 3 (0=negative or weak; 1=strong). This table wasprepared using samples from the HH breast cohort as described in Example10.

Appendix D is a table that lists exemplary antibodies whose bindingpatterns have been shown to correlate with tumor prognosis in lungcancer patients. The results are grouped into three categories that havebeen clinically recognized to be of significance: all patients,adenocarcinoma patients, and squamous cell carcinoma patients. Scoringmethods 1-3 use the following schemes: method 1 (0=negative; 1=weak;2=strong); method 2 (0=negative; 1=weak or strong); and method 3(0=negative or weak; 1=strong).

Appendix E is a table that lists exemplary antibodies whose bindingpatterns have been shown to correlate with tumor prognosis in breastcancer patients. The results are grouped into four categories that havebeen clinically recognized to be of significance: all patients, ER+patients, ER− patients, and ER+/lymph node metastases negative(ER+/node−) patients. Scoring methods 1-3 use the following schemes:method 1 (0=negative; 1=weak; 2=strong); method 2 (0=negative; 1=weak orstrong); and method 3 (0=negative or weak; 1=strong). This table wasprepared using samples from the HH breast cohort as described in Example12. Appendix E differs from Appendix C because of further analysis.

Appendix F is a table that lists exemplary antibodies whose bindingpatterns have been shown to correlate with tumor prognosis in lungcancer patients. The results are grouped into two categories that havebeen clinically recognized to be of significance: all patients andadenocarcinoma patients. Scoring methods 1-3 use the following schemes:method 1 (0=negative; 1=weak; 2=strong); method 2 (0=negative; 1=weak orstrong); and method 3 (0=negative or weak; 1=strong). This table wasprepared using samples from the HH and UAB lung cohorts as described inExample 13. The p-values and hazard ratios that were obtained with eachcohort are shown. The antibodies listed have a prognostic p-value ofless than 0.2 in both cohorts.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 depicts semi-quantitative immunohistochemistry (IHC) scoring of a298 breast cancer patient cohort with an inventive breast cancerclassification panel. The panel was prepared as described in Example2—antibodies were used as interaction partners. The patients (rows) wereclassified using k-means clustering while the antibodies (columns) wereorganized using hierarchical clustering. Dark gray represents strongpositive staining, black represents weak positive staining, while lightgray represents the absence of staining and medium gray represents alack of data. As illustrated in the Figure, nine groups of patients wereidentified by their consensus pattern of staining with the panel ofantibodies.

FIG. 2 depicts semi-quantitative immunohistochemistry (IHC) scoring of a387 lung cancer patient cohort with an inventive lung cancerclassification panel. The panel was prepared as described in Example4—antibodies were used as interaction partners. The patients (rows) wereclassified using k-means clustering while the antibodies (columns) wereorganized using hierarchical clustering. Dark gray represents strongpositive staining, black represents weak positive staining, while lightgray represents the absence of staining and medium gray represents alack of data. As illustrated in the Figure, eight groups of patientswere identified by their consensus pattern of staining with the panel ofantibodies.

FIG. 3 depicts semi-quantitative immunohistochemistry (IHC) scoring of a359 colon cancer patient cohort with an inventive colon cancerclassification panel. The panel was prepared as described in Example6—antibodies were used as interaction partners. The patients (rows) wereclassified using k-means clustering while the antibodies (columns) wereorganized using hierarchical clustering. Dark gray represents strongpositive staining, black represents weak positive staining, while lightgray represents the absence of staining and medium gray represents alack of data. As illustrated in the Figure, seven groups of patientswere identified by their consensus pattern of staining with the panel ofantibodies.

FIG. 4 shows Kaplan-Meier curves that were generated for ER+ patientsafter prognostic classification based on (A) staining with a prognosticpanel of antibodies from Appendix C and (B) the Nottingham PrognosticIndex (NPI). In each case the patients were placed into one of threeprognostic groups, namely “poor” (bottom curve), “moderate” (middlecurve) and “good” (top curve).

FIG. 5 shows Kaplan-Meier curves that were generated for ER+/node−patients after prognostic classification based on (A) staining with aprognostic panel of antibodies from Appendix C and (B) the NottinghamPrognostic Index (NPI). In each case the patients were placed into oneof three prognostic groups, namely “poor” (bottom curve), “moderate”(middle curve) and “good” (top curve). Note that under the NPI schemeER+/node− patients are never categorized as having a “poor” prognosis.For this reason, FIG. 5B only includes curves for patients with a“moderate” or “good” prognosis.

FIG. 6 shows Kaplan-Meier curves that were generated for ER+/node−patients after prognostic classification based on staining with theexemplary prognostic panel of antibodies from Table 5. In each case thepatients were placed into one of three prognostic groups, namely “bad”(bottom curve), “moderate” (middle curve) and “good” (top curve).

FIG. 7 shows Kaplan-Meier curves that were generated for ER− patientsafter prognostic classification based on staining with the exemplaryprognostic panel of antibodies from Table 6. In each case the patientswere placed into one of three prognostic groups, namely “bad” (bottomcurve), “moderate” (middle curve) and “good” (top curve).

FIG. 8 shows Kaplan-Meier curves that were generated for ER− patientsafter prognostic classification based on staining with the exemplaryprognostic panel of antibodies from Table 7. In each case the patientswere placed into one of three prognostic groups, namely “bad” (bottomcurve), “moderate” (middle curve) and “good” (top curve).

FIG. 9 shows a dendrogram for the tree panel of Table 8 that may be usedfor the prognostic classification of ER+/node− patients. If a patient ispositive for staining at a given node his or her prognosis decision treefollows the branch marked with a “+”. Conversely if a patient isnegative for staining at a given node his or her prognosis decision treefollows the branch marked “−”. This is done until a terminus is reached.

FIG. 10 shows Kaplan-Meier curves that were generated for ER+/node−patients after prognostic classification based on staining with theexemplary prognostic panel of antibodies from Table 8. In each case thepatients were placed into one of three prognostic groups, namely “bad”(bottom curve), “moderate” (middle curve) and “good” (top curve).

FIG. 11 shows a dendrogram for the tree panels of Table 9 that may beused for the prognostic classification of ER+ and ER− patients. If apatient is positive for staining at a given node his or her prognosisdecision tree follows the branch marked with a “+”. Conversely if apatient is negative for staining at a given node his or her prognosisdecision tree follows the branch marked “−”. This is done until aterminus is reached.

FIG. 12 shows Kaplan-Meier curves that were generated for combined lungcancer patients (HH cohort) after prognostic classification with theexemplary prognostic panels of antibodies from Tables 10 and 11. In eachcase the patients were placed into one of three prognostic groups,namely “bad” (bottom curve), “moderate” (middle curve) and “good” (topcurve).

FIG. 13 shows the curves that were obtained when patients in the“moderate” and “bad” groups of FIG. 12 were combined into a single “bad”prognostic group.

FIG. 14 shows Kaplan-Meier curves that were generated for combined lungcancer patients (UAB cohort) after prognostic classification with theexemplary prognostic panels of antibodies from Tables 10 and 11. In eachcase the patients were placed into one of three prognostic groups,namely “bad” (bottom curve), “moderate” (middle curve) and “good” (topcurve).

FIG. 15 shows the curves that were obtained when the patients in the“moderate” and “bad” groups of FIG. 14 were combined into a single “bad”prognostic group.

FIG. 16 shows Kaplan-Meier curves that were generated for adenocarcinomapatients (UAB cohort) after prognostic classification with the exemplaryprognostic panels of antibodies from Table 11. In each case the patientswere placed into one of three prognostic groups, namely “bad” (bottomcurve), “moderate” (middle curve) and “good” (top curve).

FIG. 17 shows Kaplan-Meier curves that were generated for squamous cellcarcinoma patients (UAB cohort) after prognostic classification with theexemplary prognostic panels of antibodies from Table 10. In each casethe patients were placed into one of three prognostic groups, namely“bad” (bottom curve), “moderate” (middle curve) and “good” (top curve).

FIG. 18 shows the relative proportions of different lung cancermorphologies that were identified in seven sub-classes of patients inthe HH lung cohort.

DEFINITIONS

Associated—When an interaction partner and a tumor marker are physically“associated” with one another as described herein, they are linked bydirect non-covalent interactions. Desirable non-covalent interactionsinclude those of the type which occur between an immunoglobulin moleculeand an antigen for which the immunoglobulin is specific, for example,ionic interactions, hydrogen bonds, van der Waals interactions,hydrophobic interactions, etc. The strength, or affinity of the physicalassociation can be expressed in terms of the dissociation constant(K_(d)) of the interaction, wherein a smaller K_(d) represents a greateraffinity. The association properties of selected interaction partnersand tumor markers can be quantified using methods well known in the art(e.g., see Davies et al., Annual Rev. Biochem. 59:439, 1990).

Classification panel—A “classification panel” of interaction partners isa set of interaction partners whose collective pattern of binding orlack of binding to a tumor sample, when taken together, is sufficient toclassify the tumor sample as a member of a particular class or subclassof tumor, or as not a member of a particular class or subclass of tumor.

Correlation—“Correlation” refers to the degree to which one variable canbe predicted from another variable, e.g., the degree to which apatient's therapeutic response can be predicted from the pattern ofbinding between a set of interaction partners and a tumor sample takenfrom that patient. A variety of statistical methods may be used tomeasure correlation between two variables, e.g., without limitation thestudent t-test, the Fisher exact test, the Pearson correlationcoefficient, the Spearman correlation coefficient, the Chi squared test,etc. Results are traditionally given as a measured correlationcoefficient with a p-value that provides a measure of the likelihoodthat the correlation arose by chance. A correlation with a p-value thatis less than 0.05 is generally considered to be statisticallysignificant. Preferred correlations have p-values that are less than0.01, especially less than 0.001.

Interaction partner—An “interaction partner” is an entity thatphysically associates with a tumor marker. For example and withoutlimitation, an interaction partner may be an antibody or a fragmentthereof that physically associates with a tumor marker. In general, aninteraction partner is said to “associate specifically” with a tumormarker if it associates at a detectable level with the tumor marker anddoes not associate detectably with unrelated molecular entities (e.g.,other tumor markers) under similar conditions. Specific associationbetween a tumor marker and an interaction partner will typically bedependent upon the presence of a particular structural feature of thetarget tumor marker such as an antigenic determinant or epitoperecognized by the interaction partner. Generally, if an interactionpartner is specific for epitope A, the presence of a molecular entity(e.g., a protein) containing epitope A or the presence of free unlabeledA in a reaction containing both free labeled A and the interactionpartner thereto, will reduce the amount of labeled A that binds to theinteraction partner. In general, it is to be understood that specificityneed not be absolute. For example, it is well known in the art thatantibodies frequently cross-react with other epitopes in addition to thetarget epitope. Such cross-reactivity may be acceptable depending uponthe application for which the interaction partner is to be used. Thusthe degree of specificity of an interaction partner will depend on thecontext in which it is being used. In general, an interaction partnerexhibits specificity for a particular tumor marker if it favors bindingwith that partner above binding with other potential partners, e.g.,other tumor markers. One of ordinary skill in the art will be able toselect interaction partners having a sufficient degree of specificity toperform appropriately in any given application (e.g., for detection of atarget tumor marker, for therapeutic purposes, etc.). It is also to beunderstood that specificity may be evaluated in the context ofadditional factors such as the affinity of the interaction partner forthe target tumor marker versus the affinity of the interaction partnerfor other potential partners, e.g., other tumor markers. If aninteraction partner exhibits a high affinity for a target tumor markerand low affinity for non-target molecules, the interaction partner willlikely be an acceptable reagent for diagnostic purposes even if it lacksspecificity. It will be appreciated that once the specificity of aninteraction partner is established in one or more contexts, it may beemployed in other, preferably similar, contexts without necessarilyre-evaluating its specificity.

Predictive panel—A “predictive panel” of interaction partners is a setof interaction partners whose collective pattern of binding or lack ofbinding to a tumor sample, when taken together, has sufficientcorrelation to classify the tumor sample as being from a patient who islikely (or not) to respond to a given therapeutic regimen.

Prognostic panel—A “prognostic panel” of interaction partners is a setof interaction partners whose collective pattern of binding or lack ofbinding to a tumor sample, when taken together, has sufficientcorrelation to classify the tumor sample as being from a patient who islikely to have a given outcome. Generally, “outcome” may include, but isnot limited to, the average life expectancy of the patient, thelikelihood that the patient will survive for a given amount of time(e.g., 6 months, 1 year, 5 years, etc.), the likelihood of recurrence,the likelihood that the patient will be disease-free for a specifiedprolonged period of time, or the likelihood that the patient will becured of the disease.

Response—The “response” of a tumor or a cancer to therapy may representany detectable change, for example at the molecular, cellular,organellar, or organismal level. For instance, tumor size, patient lifeexpectancy, recurrence, or the length of time the patient survives,etc., are all responses. Responses can be measured in any of a varietyof ways, including for example non-invasive measuring of tumor size(e.g., CT scan, image-enhanced visualization, etc.), invasive measuringof tumor size (e.g., residual tumor resection, etc.), surrogate markermeasurement (e.g., serum PSA, etc.), clinical course variance (e.g.,measurement of patient quality of life, time to relapse, survival time,etc.).

Small molecule—A “small molecule” is a non-polymeric molecule. A smallmolecule can be synthesized in a laboratory (e.g., by combinatorialsynthesis) or found in nature (e.g., a natural product). A smallmolecule is typically characterized in that it contains severalcarbon-carbon bonds and has a molecular weight of less than about 1500Da, although this characterization is not intended to be limiting forthe purposes of the present invention.

Tumor markers—“Tumor markers” are molecular entities that are detectablein tumor samples. Generally, tumor markers will be proteins that arepresent within the tumor sample, e.g., within the cytoplasm or membranesof tumor cells and/or secreted from such cells. According to the presentinvention, sets of tumor markers that correlate with tumor class orsubclass are identified. Thus, subsequent tumor samples may beclassified or subclassified based on the presence of these sets of tumormarkers.

Tumor sample—As used herein the term “tumor sample” is taken broadly toinclude cell or tissue samples removed from a tumor, cells (or theirprogeny) derived from a tumor that may be located elsewhere in the body(e.g., cells in the bloodstream or at a site of metastasis), or anymaterial derived by processing such a sample. Derived tumor samples mayinclude, for example, nucleic acids or proteins extracted from thesample.

DETAILED DESCRIPTION OF CERTAIN PREFERRED EMBODIMENTS OF THE INVENTION

As noted above, the present invention provides techniques and reagentsfor the classification and subclassification, of tumors. Suchclassification (or subclassification) has many beneficial applications.For example, a particular tumor class or subclass may correlate withprognosis and/or susceptibility to a particular therapeutic regimen. Assuch, the classification or subclassification may be used as the basisfor a prognostic or predictive kit and may also be used as the basis foridentifying previously unappreciated therapies. Therapies that areeffective against only a particular class or subclass of tumor may havebeen lost in studies whose data were not stratified by subclass; thepresent invention allows such data to be re-stratified, and allowsadditional studies to be performed, so that class- or subclass-specifictherapies may be identified and/or implemented. Alternatively oradditionally, the present invention allows identification and/orimplementation of therapies that are targeted to genes identified asclass- or subclass-specific.

Classification and Subclassification of Tumors

In general, according to the present invention, tumors are classified orsubclassified on the basis of tumor markers whose presence is correlatedwith a particular class or subclass. In preferred embodiments, the tumormarkers are detected via their physical association with an interactionpartner. Included in the present invention are kits comprising sets ofinteraction partners that together can be used to identify or classify aparticular tumor sample; such sets are generally referred to as“classification panels”.

The present invention provides systems of identifying classificationpanels. In general, tumor samples are contacted with individualinteraction partners, and binding between the interaction partners andtheir cognate tumor markers is detected. For example, panels ofinteraction partners that identify a particular class or subclass oftumor within tumor samples of a selected tissue of origin may be definedby contacting individual interaction partners with a variety ofdifferent tumor samples (e.g., from different patients) all of the sametissue of origin. Individual interaction partners may be selected forinclusion in the ultimate classification panel based on their binding toonly a subset of the tumor samples (e.g., see Examples 1-4). Those ofordinary skill in the art, however, will appreciate that all that isrequired for a collection of interaction partners to operate effectivelyas a classification panel is that the combined binding characteristicsof member interaction partners together are sufficient to classify aparticular tumor sample.

The inventive process of identifying useful panels of interactionpartners as described herein may itself result in the identification ofnew tumor classes or subclasses. That is, through the process ofanalyzing interaction partner binding patterns, investigators will oftendiscover new tumor classes or subclasses to which sets of interactionpartners bind. Thus, the processes (a) of defining classification panelsof interaction partners for given tumor classes or subclasses; and (b)identifying new tumor classes or subclasses may well be experimentallyinterrelated. In general, the greater the number of tumor samplestested, the greater the likelihood that new classes or subclasses willbe defined.

Often, when identifying sets of interaction partners that can act as aclassification (or subclassification) panel, it will be desirable toobtain the largest set of tumor samples possible, and also to collectthe largest amount of information possible about the individual samples.For example, the origin of the tumor, the gender of the patient, the ageof the patient, the staging of the tumor (e.g., according to the TNMsystem), any microscopic or submicroscopic characteristics of the tumorthat may have been determined, may be recorded. Those of ordinary skillin the art will appreciate that the more information that is known abouta tumor sample, the more aspects of that sample are available forcorrelation with interaction partner binding.

The systems of the present invention have particular utility inclassifying or subclassifying tumor samples that are not otherwisedistinguishable from one another. Thus, in some embodiments, it will bedesirable to analyze the largest collection of tumor samples that aremost similar to one another.

When obtaining tumor samples for testing according to the presentinvention, it is generally preferred that the samples represent orreflect characteristics of a population of patients or samples. It mayalso be useful to handle and process the samples under conditions andaccording to techniques common to clinical laboratories. Although thepresent invention is not intended to be limited to the strategies usedfor processing tumor samples, we note that, in the field of pathology,it is often common to fix samples in buffered formalin, and then todehydrate them by immersion in increasing concentrations of ethanolfollowed by xylene. Samples are then embedded into paraffin, which isthen molded into a “paraffin block” that is a standard intermediate inhistologic processing of tissue samples. The present inventors havefound that many useful interaction partners display comparable bindingregardless of the method of preparation of tumor samples; those ofordinary skill in the art can readily adjust observations to account fordifferences in preparation procedure.

In preferred embodiments of the invention, large numbers of tissuesamples are analyzed simultaneously. In some embodiments, a tissue arrayis prepared. Tissue arrays may be constructed according to a variety oftechniques. According to one procedure, a commercially-availablemechanical device (e.g., the manual tissue arrayer MTA1 from BeecherInstruments of Sun Prairie, Wis.) is used to remove an0.6-micron-diameter, full thickness “core” from a paraffin block (thedonor block) prepared from each patient, and to insert the core into aseparate paraffin block (the recipient block) in a designated locationon a grid. In preferred embodiments, cores from as many as about 400patients can be inserted into a single recipient block; preferably,core-to-core spacing is approximately 1 mm. The resulting tissue arraymay be processed into thin sections for staining with interactionpartners according to standard methods applicable to paraffin embeddedmaterial. Depending upon the thickness of the donor blocks, as well asthe dimensions of the clinical material, a single tissue array can yieldabout 50-150 slides containing >75% relevant tumor material forassessment with interaction partners. Construction of two or moreparallel tissue arrays of cores from the same cohort of patient samplescan provide relevant tumor material from the same set of patients induplicate or more. Of course, in some cases, additional samples will bepresent in one array and not another.

The present inventors have found that it is often desirable to evaluatesome aspects of the binding characteristics of potential interactionpartners before or while assessing the desirability of including them inan interaction panel. For example, the inventors have found that it isoften desirable to perform a titration study in which differentconcentrations of the interaction partner are contacted with a diverseset of tissue samples derived from a variety of different tissues (e.g.,normal and/or tumor) in order to identify a concentration or titer atwhich differential binding is observed. This titer is referred to hereinas a “discriminating titer”. Such differential staining may be observedbetween different tissue samples and/or between different cell typeswithin a given tissue sample.

In general, any tissue sample may be used for this purpose (e.g.,samples obtained from the epididymis, esophagus, gall bladder, kidneys,liver, lungs, lymph nodes, muscles, ovaries, pancreas, parathyroidglands, placenta, prostate, saliva, skin, spleen, stomach, testis,thymus, thyroid, tonsils, uterus, etc.). For such titration studies,greater diversity among samples is often preferred. Without intending tolimit the present invention, the inventors observe that useful titersfor particular interaction partners can typically be defined in a studyof approximately 40-70 different tissue samples from about 20-40different tissues.

Binding studies (for titration, for assessment of inclusion in a panel,or during use of a panel) may be performed in any format that allowsspecific interaction to be detected. Where large numbers of samples areto be handled, it may be desirable to utilize arrayed and/or automatedformats. Particularly preferred formats include tissue arrays asdiscussed above. The staining of large numbers of samples derived from avariety of tumors in a tissue array format allows excellent comparativeassessment of differential staining between or among samples underidentical conditions. According to the present invention, stainingpatterns that identify at least about 10% of samples as binding with aparticular interaction partner, or at least about 20, 30, 40, 50% ormore of samples, are likely to represent “real” differential stainingpatterns (i.e., real variations in binding with interaction partner andnot experimental variations, for example, due to sample processing orday to day variation in staining techniques).

Any available technique may be used to detect binding between aninteraction partner and a tumor sample. One powerful and commonly usedtechnique is to have a detectable label associated (directly orindirectly) with the interaction partner. For example, commonly-usedlabels that often are associated with antibodies used in binding studiesinclude fluorochromes, enzymes, gold, iodine, etc. Tissue staining bybound interaction partners is then assessed, preferably by a trainedpathologist or cytotechnologist. For example, a scoring system may beutilized to designate whether the interaction partner does or does notbind to (e.g., stain) the sample, whether it stains the sample stronglyor weakly and/or whether useful information could not be obtained (e.g.,because the sample was lost, there was no tumor in the sample or theresult was otherwise ambiguous). Those of ordinary skill in the art willrecognize that the precise characteristics of the scoring system are notcritical to the invention. For example, staining may be assessedqualitatively or quantitatively; more or less subtle gradations ofstaining may be defined; etc.

Whatever the format, and whatever the detection strategy, identificationof a discriminating titer can simplify binding studies to assess thedesirability of including a given interaction partner in a panel. Insuch studies, the interaction partner is contacted with a plurality ofdifferent tumor samples that preferably have at least one common trait(e.g., tissue of origin), and often have multiple common traits (e.g.,tissue of origin, stage, microscopic characteristics, etc.). In somecases, it will be desirable to select a group of samples with at leastone common trait and at least one different trait, so that a panel ofinteraction partners is defined that distinguishes the different trait.In other cases, it will be desirable to select a group of samples withno detectable different traits, so that a panel of interaction partnersis defined that distinguishes among previously indistinguishablesamples. Those of ordinary skill in the art will understand, however,that the present invention often will allow both of these goals to beaccomplished even in studies of sample collections with varying degreesof similarity and difference.

According to the present invention, interaction partners that bind totumor samples may be characterized by their ability to discriminateamong tumor samples. Any of a variety of techniques may be used toidentify discriminating interaction partners. To give but one example,the present inventors have found it useful to define a “consensus panel”of tissue samples for tumors of a particular tissue of origin (seeExamples 2-6). Those of ordinary skill in the art will again appreciatethat the precise parameters used to designate a particular sample asinterpretable and reproducible are not critical to the invention.Interaction partners may then be classified based on their ability todiscriminate among tissue samples in the consensus panel (see Examples2-6).

Assessing Prognosis or Therapeutic Regimen

The present invention further provides systems for identifying panels ofinteraction partners whose binding correlates with factors beyond tumorclass or subclass, such as likelihood of a particular favorable orunfavorable outcome, susceptibility (or lack thereof) to a particulartherapeutic regimen, etc.

As mentioned in the background, current approaches to assigningprognostic probabilities and/or selecting appropriate therapeuticregimens for particular tumors generally utilize thetumor-node-metastasis (TNM) system. This system uses the size of thetumor, the presence or absence of tumor in regional lymph nodes and thepresence or absence of distant metastases, to assign a stage to thetumor. The assigned stage is used as a basis for selection ofappropriate therapy and for prognostic purposes.

The present invention provides new methods and systems for evaluatingtumor prognosis and/or recommended therapeutic approaches. Inparticular, the present invention provides systems for identifyingpanels of interaction partners whose binding correlates with tumorprognosis or therapeutic outcome.

For example, interaction partners whose binding correlates withprognosis can be identified by evaluating their binding to a collectionof tumor samples for which prognosis is known or knowable. That is, thestrategies of the invention may be employed either to identifycollections of interaction partners whose binding correlates with aknown outcome, or may be employed to identify a differential stainingpattern that is then correlated with outcome (which outcome may eitherbe known in advance or determined over time).

In general, it is preferred that inventive binding analyses be performedon human tumor samples. However, it is not necessary that the humantumors grow in a human host. Particularly for studies in which long-termoutcome data are of interest (especially prognostic or predictivestudies), it can be particularly useful to analyze samples grown invitro (e.g., cell lines) or, more preferably, in a non-human host (e.g.,a rodent, a dog, a sheep, a pig, or other animal). For instance, Example9 provides a description of an assay in which inventive techniquesemploying human tumor cells growing in a non-human host are employed todefine and/or utilize a panel of interaction partners whose binding totumor samples correlates with prognosis and/or responsiveness totherapy.

It will often be desirable, when identifying interaction partners whosebinding correlates with prognosis, to collect information abouttreatment regimens that may have been applied to the tumor whose sampleis being assessed, in order to control for effects attributable to tumortherapy. Prognostic panel binding may correlate with outcome independentof treatment (Hayes et al., J. Mamm. Gland Bio. Neo. 6:375, 2001). Manyprognostic markers, however, have both prognostic and predictivecharacter (e.g., Her2/Neu status). Many of the individual interactionpartners that comprise a prognostic panel may likewise have predictivecapability and/or be members of a predictive panel.

Those of ordinary skill in the art will appreciate that prognosticpanels (or individual interaction partners) have greater clinicalutility if their binding/lack thereof correlates with positive/negativeoutcomes that are well separated statistically.

The inventive strategies may also be applied to the identification ofpredictive panels of interaction partners (i.e., panels whose bindingcorrelates with susceptibility to a particular therapy). As noted above,some prognostic panels may also have predictive capabilities.

Interaction partners to be included in predictive panels are identifiedin binding studies performed on tumor samples that do or do not respondto a particular therapy. As with the prognostic panels, predictivepanels may be assembled based on tests of tumor samples whoseresponsiveness is already known, or on samples whose responsiveness isnot known in advance. As with the prognostic studies discussed above,the source of the tumor samples is not essential and can include, forexample, tumor cell lines whose responsiveness to particular chemicalagents has been determined, tumor samples from animal models in whichtumors have been artificially introduced and therapeutic responsivenesshas been determined and/or samples from naturally-occurring (human orother animal) tumors for which outcome data (e.g., time of survival,responsiveness to therapy, etc.) are available. Panels of interactionpartners whose binding to tumor samples correlates with any prognosticor therapeutic trend can be defined and utilized as described herein.

Once correlations between interaction partner binding and tumor behaviorhave been established, the defined prognostic or predictive panels canbe used to evaluate and classify tumor samples from patients and can berelied upon, for example to guide selection of an effective therapeuticregimen. As with the tumor classification studies described above, theprocess of identifying interaction partner panels whose bindingcorrelates with outcome may itself identify particular outcomes notpreviously appreciated as distinct.

Those of ordinary skill in the art will appreciate that it is likelythat, in at least some instances, tumor class or subclass identity willitself correlate with prognosis or responsiveness. In suchcircumstances, it is possible that the same set of interaction partnerscan act as both a classification panel and a prognosis or predictivepanel.

Tumor Elements Bound By Interaction Partners

The inventive strategies for identifying and utilizing interactionpartner panels in classifying or analyzing tumor samples do not rely onany assumptions about the identity or characteristics of the tumorcomponents bound by the interaction partners. So long as interactionpartner binding within the relevant panel correlates with some featureof interest, the inventive teachings apply. In many if not most, cases,however, it is expected that binding will be with a protein expressed bytumor cells.

In some preferred embodiments of the invention, interaction partnersbind to tumor markers that (a) are differentially expressed in tumorcells; (b) are members of protein families whose activities contributeto relevant biological events (e.g., gene families that have beenimplicated in cancer such as oncogenes, tumor suppressor genes, andgenes that regulate apoptosis; gene families that have been implicatedin drug resistance; etc.); (c) are present on or in the plasma membraneof the tumor cells; and/or (d) are the products of degradation of tumorcomponents, which degradation products might be detectable in patientserum.

In fact, according to the present invention, interaction partners foranalysis and use in inventive panels may sometimes be identified byfirst identifying a tumor-associated protein of interest, and thenfinding a potential interaction partner that binds with the protein.Binding by this potential interaction partner to tumor samples may thenbe assessed and utilized as described herein.

For example, as described in the Examples, the present inventors havesuccessfully assembled classification panels comprised of antibodiesthat bind to tumor protein antigens. Candidate antigens were identifiedboth through literature reviews of proteins that play a biological rolein tumor initiation or progression, or that are known to bedifferentially expressed in tumors, and through gene expression studiesthat identified additional differentially expressed proteins.

Work by the present inventors, as well as by others, has alreadydemonstrated that studies of gene expression patterns in large tumorcohorts can identify novel tumor classes (see, for example, Perou etal., Nature 406:747, 2000; Sorlie et al., Proc Natl Acad. Sci. USA98:10869, 2001; van't Veer et al., Nature 415:530, 2002; West et al.,Proc Natl. Acad. Sci. USA 98:11462, 2001; Hedenfalk et al., N. Engl. J.Med. 344:539, 2001; Gruvberger et al., Cancer Res. 61:5979, 2001;MacDonald et al., Nature Genet. 29:143, 2001; Pomeroy et al., Nature415:436, 2002; Jazaeri et al., J. Natl Cancer Inst 94:990, 2002; Welshet al., Proc. Natl. Acad. Sci. USA 98:1176, 2001; Wang et al., Gene229:101, 1999; Beer et al., Nature Med. 8:816, 2002; Garber et al., ProcNatl Acad Sci USA 98:13784, 2001; Bhattacharjee et al., Proc Natl AcadSci USA 98:13790, 2001; Zou et al., Oncogene 21:4855, 2002; Lin et al.,Oncogene 21:4120, 2002; Alon et al., Proc Natl Acad Sci USA 96:6745,1999; Takahashi et al., Proc Natl Acad Sci USA 98:9754, 2001; Singh etal., Cancer Cell 1:203, 2002; LaTulippe et al., Cancer Res. 62:4499,2002; Welsh et al., Cancer Res. 61:5974, 2001; Dhanasekaran et al.,Nature 412:822, 2001; Hippo et al., Cancer Res. 62:233, 2002; Yeoh etal., Cancer Cell 1:133, 2002; Hofmann et al., Lancet 359:481, 2002;Ferrando et al., Cancer Cell 1:75, 2002; Shipp et al., Nature Med 8:68,2002; Rosenwald et al., N. Engl. J. Med. 346:1937, 2002; and Alizadeh etal., Nature 403:503, 2000, each of which is incorporated herein byreference).

The gene sets described in these publications are promising candidatesfor genes that are likely to encode tumor markers whose interactionpartners are useful in tumor classification and subclassificationaccording to the present invention. Of particular interest are gene setsdifferentially expressed in solid tumors.

Furthermore, in general, given that differentially expressed genes arelikely to be responsible for the different phenotypic characteristics oftumors, the present invention recognizes that such genes will oftenencode tumor markers for which a useful interaction partner, thatdiscriminates among tumor classes or subclasses, can likely be prepared.A differentially expressed gene is a gene whose transcript abundancevaries between different samples, e.g., between different tumor samples,between normal versus tumor samples, etc. In general, the amount bywhich the expression varies and the number of samples in which theexpression varies by that amount will depend upon the number of samplesand the particular characteristics of the samples. One skilled in theart will be able to determine, based on knowledge of the samples, whatconstitutes a significant degree of differential expression. Such genescan be identified by any of a variety of techniques including, forinstance, in situ hybridization, Northern blot, nucleic acidamplification techniques (e.g., PCR, quantitative PCR, the ligase chainreaction, etc.), and, most commonly, microarray analysis.

Furthermore, those of ordinary skill in the art will readily appreciate,reading the present disclosure, that the inventive processes describedherein of identifying and/or using sets of interaction partners whosebinding (or lack thereof) correlates with an interesting tumor feature(e.g., tumor type or subtype, patient outcome, responsiveness of tumoror patient to therapy, etc.) inherently identifies both interactionpartners of interest and the tumor markers to which they bind. Thus, oneimportant aspect of the present invention is the identification of tumormarkers whose ability (or lack thereof) to associate with an interactionpartner correlates with a tumor characteristic of interest. Such tumormarkers are useful as targets for identification of new therapeuticreagents, as well as of additional interaction partners useful in thepractice of the present invention. Thus, it is to be understood thatdiscussions of interaction partners presented herein are typically notlimited to a particular interaction partner compound or entity, but maybe generalized to include any compound or entity that binds to therelevant tumor marker(s) with requisite specificity and affinity.

Preparation of Interaction Partners

In general, interaction partners are entities that physically associatewith selected tumor markers. Thus, any entity that binds detectably to atumor marker may be utilized as an interaction partner in accordancewith the present invention, so long as it binds with an appropriatecombination of affinity and specificity.

Particularly preferred interaction partners are antibodies, or fragments(e.g., F(ab) fragments, F(ab′)₂ fragments, Fv fragments, or sFvfragments, etc.; see, for example, Inbar et al., Proc. Nat. Acad. Sci.USA 69:2659, 1972; Hochman et al., Biochem. 15:2706, 1976; and Ehrlichet al., Biochem. 19:4091, 1980; Huston et al., Proc. Nat. Acad. Sci. USA85:5879, 1998; U.S. Pat. Nos. 5,091,513 and 5,132,405 to Huston et al.;and U.S. Pat. No. 4,946,778 to Ladner et al., each of which isincorporated herein by reference). In certain embodiments, interactionpartners may be selected from libraries of mutant antibodies (orfragments thereof). For example, collections of antibodies that eachinclude different point mutations may be screened for their associationwith a tumor marker of interest. Yet further, chimeric antibodies may beused as interaction partners, e.g., “humanized” or “veneered” antibodiesas described in greater detail below.

It is to be understood that the present invention is not limited tousing antibodies or antibody fragments as interaction partners ofinventive tumor markers. In particular, the present invention alsoencompasses the use of synthetic interaction partners that mimic thefunctions of antibodies. Several approaches to designing and/oridentifying antibody mimics have been proposed and demonstrated (e.g.,see the reviews by Hsieh-Wilson et al., Acc. Chem. Res. 29:164, 2000 andPeczuh and Hamilton, Chem. Rev. 100:2479, 2000). For example, smallmolecules that bind protein surfaces in a fashion similar to that ofnatural proteins have been identified by screening synthetic librariesof small molecules or natural product isolates (e.g., see Gallop et al.,J. Med. Chem. 37:1233, 1994; Gordon et al., J. Med. Chem. 37:1385, 1994;DeWitt et al., Proc. Natl. Acad. Sci. U.S.A. 90:6909, 1993; Bunin etal., Proc. Natl. Acad. Sci. U.S.A. 91:4708, 1994; Virgilio and Ellman,J. Am. Chem. Soc. 116:11580, 1994; Wang et al., J. Med. Chem. 38:2995,1995; and Kick and Ellman, J. Med. Chem. 38:1427, 1995). Similarly,combinatorial approaches have been successfully applied to screenlibraries of peptides and polypeptides for their ability to bind a rangeof proteins (e.g., see Cull et al., Proc. Natl. Acad. Sci. U.S.A.89:1865, 1992; Mattheakis et al., Proc. Natl. Acad. Sci. U.S.A. 91:9022,1994; Scott and Smith, Science 249:386, 1990; Devlin et al., Science249:404, 1990; Corey et al., Gene 128:129, 1993; Bray et al.,Tetrahedron Lett. 31:5811, 1990; Fodor et al., Science 251:767, 1991;Houghten et al., Nature 354:84, 1991; Lam et al., Nature 354:82, 1991;Blake and Litzi-Davis, Bioconjugate Chem. 3:510, 1992; Needels et al.,Proc. Natl. Acad. Sci. U.S.A. 90:10700, 1993; and Ohlmeyer et al., Proc.Natl. Acad. Sci. U.S.A. 90:10922, 1993). Similar approaches have alsobeen used to study carbohydrate-protein interactions (e.g., seeOldenburg et al., Proc. Natl. Acad. Sci. U.S.A. 89:5393, 1992) andpolynucleotide-protein interactions (e.g., see Ellington and Szostak,Nature 346:818, 1990 and Tuerk and Gold, Science 249:505, 1990). Theseapproaches have also been extended to study interactions betweenproteins and unnatural biopolymers such as oligocarbamates, oligoureas,oligosulfones, etc. (e.g., see Zuckermann et al., J. Am. Chem. Soc.114:10646, 1992; Simon et al., Proc. Natl. Acad. Sci. U.S.A. 89:9367,1992; Zuckermann et al., J. Med. Chem. 37:2678, 1994; Burgess et al.,Angew. Chem., Int. Ed. Engi. 34:907, 1995; and Cho et al., Science261:1303, 1993). Yet further, alternative protein scaffolds that areloosely based around the basic fold of antibody molecules have beensuggested and may be used in the preparation of inventive interactionpartners (e.g., see Ku and Schultz Proc. Natl. Acad. Sci. U.S.A.92:6552, 1995). Antibody mimics comprising a scaffold of a smallmolecule such as 3-aminomethylbenzoic acid and a substituent consistingof a single peptide loop have also been constructed. The peptide loopperforms the binding function in these mimics (e.g., see Smythe et al.,J. Am. Chem. Soc. 116:2725, 1994). A synthetic antibody mimic comprisingmultiple peptide loops built around a calixarene unit has also beendescribed (e.g., see U.S. Pat. No. 5,770,380 to Hamilton et al.).

Detecting Association of Interaction Partners and Tumor Markers

Any available strategy or system may be utilized to detect associationbetween an interaction partner and its cognate tumor marker. In certainembodiments, association can be detected by adding a detectable label tothe interaction partner. In other embodiments, association can bedetected by using a labeled secondary interaction partner thatassociates specifically with the primary interaction partner, e.g., asis well known in the art of antigen/antibody detection. The detectablelabel may be directly detectable or indirectly detectable, e.g., throughcombined action with one or more additional members of a signalproducing system. Examples of directly detectable labels includeradioactive, paramagnetic, fluorescent, light scattering, absorptive andcolorimetric labels. Examples of indirectly detectable includechemiluminescent labels, e.g., enzymes that are capable of converting asubstrate to a chromogenic product such as alkaline phosphatase,horseradish peroxidase and the like.

Once a labeled interaction partner has bound a tumor marker, the complexmay be visualized or detected in a variety of ways, with the particularmanner of detection being chosen based on the particular detectablelabel, where representative detection means include, e.g., scintillationcounting, autoradiography, measurement of paramagnetism, fluorescencemeasurement, light absorption measurement, measurement of lightscattering and the like.

In general, association between an interaction partner and its cognatetumor marker may be assayed by contacting the interaction partner with atumor sample that includes the marker. Depending upon the nature of thesample, appropriate methods include, but are not limited to,immunohistochemistry (IHC), radioimmunoassay, ELISA, immunoblotting andfluorescence activates cell sorting (FACS). In the case where thepolypeptide is to be detected in a tissue sample, e.g., a biopsy sample,IHC is a particularly appropriate detection method. Techniques forobtaining tissue and cell samples and performing IHC and FACS are wellknown in the art.

The inventive strategies for classifying and/or subclassifying tumorsamples may be applied to samples of any type and of any tissue oforigin. In certain preferred embodiments of the invention, thestrategies are applied to solid tumors. Historically, researchers haveencountered difficulty in defining solid tumor subtypes, given thechallenges associated with defining their molecular characteristics. Asdemonstrated in the Examples, the present invention is particularlybeneficial in this area. Particularly preferred solid tumors include,for example, breast, lung, colon, and ovarian tumors. The invention alsoencompasses the recognition that tumor markers that are secreted fromthe cells in which they are produced may be present in serum, enablingtheir detection through a blood test rather than requiring a biopsyspecimen. An interaction partner that binds to such tumor markersrepresents a particularly preferred embodiment of the invention.

In general, the results of such an assay can be presented in any of avariety of formats. The results can be presented in a qualitativefashion. For example, the test report may indicate only whether or not aparticular tumor marker was detected, perhaps also with an indication ofthe limits of detection. Additionally the test report may indicate thesubcellular location of binding, e.g., nuclear versus cytoplasmic and/orthe relative levels of binding in these different subcellular locations.The results may be presented in a semi-quantitative fashion. Forexample, various ranges may be defined and the ranges may be assigned ascore (e.g., 0 to 5) that provides a certain degree of quantitativeinformation. Such a score may reflect various factors, e.g., the numberof cells in which the tumor marker is detected, the intensity of thesignal (which may indicate the level of expression of the tumor marker),etc. The results may be presented in a quantitative fashion, e.g., as apercentage of cells in which the tumor marker is detected, as aconcentration, etc. As will be appreciated by one of ordinary skill inthe art, the type of output provided by a test will vary depending uponthe technical limitations of the test and the biological significanceassociated with detection of the tumor marker. For example, in the caseof certain tumor markers a purely qualitative output (e.g., whether ornot the tumor marker is detected at a certain detection level) providessignificant information. In other cases a more quantitative output(e.g., a ratio of the level of expression of the tumor marker in twosamples) is necessary.

Identification of Novel Therapies

Predictive panels of interaction agents are useful according to thepresent invention not only to classify tumor samples obtained fromcancer sufferers with respect to their likely responsiveness to knowntherapies, but also to identify potential new therapies or therapeuticagents that could be useful in the treatment of cancer.

For example, as noted above, the process of identifying or usinginventive panels according to the present invention simultaneouslyidentifies and/or characterizes tumor markers in or on the tumor cellsthat correlate with one or more selected tumor characteristics (e.g.,tumor type or subtype, patient prognosis, and/or responsiveness of tumoror patient to therapy). Such tumor markers are attractive candidates foridentification of new therapeutic agents (e.g., via screens to detectcompounds or entities that bind to the tumor markers, preferably with atleast a specified affinity and/or specificity, and/or via screens todetect compounds or entities that modulate (i.e., increase or decrease)expression, localization, modification, or activity of the tumormarkers. In many instances, interaction partners themselves may prove tobe useful therapeutics.

Thus the present invention provides interaction partners that arethemselves useful therapeutic agents. For example, binding by aninteraction partner, or a collection of interaction partners, to acancer cell, might inhibit growth of that cell. Alternatively oradditionally, interaction partners defined or prepared according to thepresent invention could be used to deliver a therapeutic agent to acancer cell. In particular, interaction partners may be coupled to oneor more therapeutic agents. Suitable agents in this regard includeradionuclides and drugs. Preferred radionuclides include ⁹⁰Y, ¹²³I,¹²⁵I, ¹³¹I, ¹⁸⁶Re, ¹⁸⁸Re, ²¹¹At and ²¹²Bi. Preferred drugs includechlorambucil, ifosphamide, meclorethamine, cyclophosphamide,carboplatin, cisplatin, procarbazine, decarbazine, carmustine,cytarabine, hydroxyurea, mercaptopurine, methotrexate, thioguanine,5-fluorouracil, actinomycin D, bleomycin, daunorubicin, doxorubicin,etoposide, vinblastine, vincristine, L-asparginase,adrenocorticosteroids, canciclovir triphosphate, adeninearabinonucleoside triphosphate,5-aziridinyl-4-hydroxylamino-2-nitrobenzamide, acrolein, phosphoramidemustard, 6-methylpurine, etoposide, methotrexate, benzoic acid mustard,cyanide and nitrogen mustard.

According to such embodiments, the therapeutic agent may be coupled withan interaction partner by direct or indirect covalent or non-covalentinteractions. A direct interaction between a therapeutic agent and aninteraction partner is possible when each possesses a substituentcapable of reacting with the other. For example, a nucleophilic group,such as an amino or sulfhydryl group, on one may be capable of reactingwith a carbonyl-containing group, such as an anhydride or an acidhalide, or with an alkyl group containing a good leaving group (e.g., ahalide) on the other. Indirect interactions might involve a linker groupthat is itself associated with both the therapeutic agent and theinteraction partner. A linker group can function as a spacer to distancean interaction partner from an agent in order to avoid interference withassociation capabilities. A linker group can also serve to increase thechemical reactivity of a substituent on an agent or an interactionpartner and thus increase the coupling efficiency. An increase inchemical reactivity may also facilitate the use of agents, or functionalgroups on agents, which otherwise would not be possible.

It will be evident to those skilled in the art that a variety ofbifunctional or polyfunctional reagents, both homo- andhetero-functional (such as those described in the catalog of the PierceChemical Co., Rockford, Ill.), may be employed as the linker group.Coupling may be effected, for example, through amino groups, carboxylgroups, sulfydryl groups or oxidized carbohydrate residues. There arenumerous references describing such methodology, e.g., U.S. Pat. No.4,671,958, to Rodwell et al. It will further be appreciated that atherapeutic agent and an interaction partner may be coupled vianon-covalent interactions, e.g., ligand/receptor type interactions. Anyligand/receptor pair with a sufficient stability and specificity tooperate in the context of the invention may be employed to couple atherapeutic agent and an interaction partner. To give but an example, atherapeutic agent may be covalently linked with biotin and aninteraction partner with avidin. The strong non-covalent binding ofbiotin to avidin would then allow for coupling of the therapeutic agentand the interaction partner. Typical ligand/receptor pairs includeprotein/co-factor and enzyme/substrate pairs. Besides the commonly usedbiotin/avidin pair, these include without limitation,biotin/streptavidin, digoxigenin/anti-digoxigenin, FK506/FK506-bindingprotein (FKBP), rapamycin/FKBP, cyclophilin/cyclosporin andglutathione/glutathione transferase pairs. Other suitableligand/receptor pairs would be recognized by those skilled in the art,e.g., monoclonal antibodies paired with a epitope tag such as, withoutlimitation, glutathione-S-transferase (GST), c-myc, FLAG® and maltosebinding protein (MBP) and further those described in Kessler pp. 105-152of Advances in Mutagenesis” Ed. by Kessler, Springer-Verlag, 1990;“Affinity Chromatography: Methods and Protocols (Methods in MolecularBiology)” Ed. by Pascal Baillon, Humana Press, 2000; and “ImmobilizedAffinity Ligand Techniques” by Hermanson et al., Academic Press, 1992.

Where a therapeutic agent is more potent when free from the interactionpartner, it may be desirable to use a linker group which is cleavableduring or upon internalization into a cell. A number of differentcleavable linker groups have been described. The mechanisms for theintracellular release of an agent from these linker groups includecleavage by reduction of a disulfide bond (e.g., U.S. Pat. No. 4,489,710to Spitler), by irradiation of a photolabile bond (e.g., U.S. Pat. No.4,625,014 to Senter et al.), by hydrolysis of derivatized amino acidside chains (e.g., U.S. Pat. No. 4,638,045 to Kohn et al.), by serumcomplement-mediated hydrolysis (e.g., U.S. Pat. No. 4,671,958 to Rodwellet al.) and by acid-catalyzed hydrolysis (e.g., U.S. Pat. No. 4,569,789to Blattler et al.).

In certain embodiments, it may be desirable to couple more than onetherapeutic agent to an interaction partner. In one embodiment, multiplemolecules of an agent are coupled to one interaction partner molecule.In another embodiment, more than one type of therapeutic agent may becoupled to one interaction partner molecule. Regardless of theparticular embodiment, preparations with more than one agent may beprepared in a variety of ways. For example, more than one agent may becoupled directly to an interaction partner molecule, or linkers thatprovide multiple sites for attachment can be used.

Alternatively, a carrier can be used. A carrier may bear the agents in avariety of ways, including covalent bonding either directly or via alinker group. Suitable carriers include proteins such as albumins (e.g.,U.S. Pat. No. 4,507,234 to Kato et al.), peptides, and polysaccharidessuch as aminodextran (e.g., U.S. Pat. No. 4,699,784 to Shih et al.). Acarrier may also bear an agent by non-covalent bonding or byencapsulation, such as within a liposome vesicle (e.g., U.S. Pat. Nos.4,429,008 to Martin et al. and 4,873,088 to Mayhew et al.). Carriersspecific for radionuclide agents include radiohalogenated smallmolecules and chelating compounds. For example, U.S. Pat. No. 4,735,792to Srivastava discloses representative radiohalogenated small moleculesand their synthesis. A radionuclide chelate may be formed from chelatingcompounds that include those containing nitrogen and sulfur atoms as thedonor atoms for binding the metal, or metal oxide, radionuclide. Forexample, U.S. Pat. No. 4,673,562 to Davison et al. disclosesrepresentative chelating compounds and their synthesis.

When interaction partners are themselves therapeutics, it will beunderstood that, in many cases, any interaction partner that binds withthe same tumor marker may be so used.

In one preferred embodiment of the invention, the therapeutic agents(whether interaction partners or otherwise) are antibodies. As is wellknown in the art, when using an antibody or fragment thereof fortherapeutic purposes it may prove advantageous to use a “humanized” or“veneered” version of an antibody of interest to reduce any potentialimmunogenic reaction. In general, “humanized” or “veneered” antibodymolecules and fragments thereof minimize unwanted immunologicalresponses toward antihuman antibody molecules which can limit theduration and effectiveness of therapeutic applications of those moietiesin human recipients.

A number of “humanized” antibody molecules comprising an antigen bindingportion derived from a non-human immunoglobulin have been described inthe art, including chimeric antibodies having rodent variable regionsand their associated complementarity-determining regions (CDRs) fused tohuman constant domains (e.g., see Winter et al., Nature 349:293, 1991;Lobuglio et al., Proc. Nat. Acad. Sci. USA 86:4220, 1989; Shaw et al.,J. Immunol. 138:4534, 1987; and Brown et al., Cancer Res. 47:3577,1987), rodent CDRs grafted into a human supporting framework region (FR)prior to fusion with an appropriate human antibody constant domain(e.g., see Riechmann et al., Nature 332:323, 1988; Verhoeyen et al.,Science 239:1534, 1988; and Jones et al. Nature 321:522, 1986) androdent CDRs supported by recombinantly veneered rodent FRs (e.g., seeEuropean Patent Publication No. 519,596, published Dec. 23, 1992). It isto be understood that the invention also encompasses “fully human”antibodies produced using the XenoMouse™ technology (AbGenix Corp.,Fremont, Calif.) according to the techniques described in U.S. Pat. No.6,075,181.

Yet further, so-called “veneered” antibodies may be used that include“veneered FRs”. The process of veneering involves selectively replacingFR residues from, e.g., a murine heavy or light chain variable region,with human FR residues in order to provide a xenogeneic moleculecomprising an antigen binding portion which retains substantially all ofthe native FR polypeptide folding structure. Veneering techniques arebased on the understanding that the antigen binding characteristics ofan antigen binding portion are determined primarily by the structure andrelative disposition of the heavy and light chain CDR sets within theantigen-association surface (e.g., see Davies et al., Ann. Rev. Biochem.59:439, 1990). Thus, antigen association specificity can be preserved ina humanized antibody only wherein the CDR structures, their interactionwith each other and their interaction with the rest of the variableregion domains are carefully maintained. By using veneering techniques,exterior (e.g., solvent-accessible) FR residues which are readilyencountered by the immune system are selectively replaced with humanresidues to provide a hybrid molecule that comprises either a weaklyimmunogenic, or substantially non-immunogenic veneered surface.

Preferably, interaction partners suitable for use as therapeutics (ortherapeutic agent carriers) exhibit high specificity for the targettumor marker and low background binding to other tumor markers. Incertain embodiments, monoclonal antibodies are preferred for therapeuticpurposes.

Tumor markers that are expressed on the cell surface represent preferredtargets for the development of therapeutic agents, particularlytherapeutic antibodies. For example, cell surface proteins can betentatively identified using sequence analysis based on the presence ofa predicted transmembrane domain. Their presence on the cell surface canultimately be confirmed using IHC.

Kits

Useful sets or panels of interaction partners according to the presentinvention may be prepared and packaged together in kits for use inclassifying, diagnosing, or otherwise characterizing tumor samples, orfor inhibiting tumor cell growth or otherwise treating cancer.

Any available technique may be utilized in the preparation of individualinteraction partners for inclusion in kits. For example, protein orpolypeptide interaction partners may be produced by cells (e.g.,recombinantly or otherwise), may be chemically synthesized, or may beotherwise generated in vitro (e.g., via in vitro transcription and/ortranslation). Non-protein or polypeptide interaction partners (e.g.,small molecules, etc.) may be synthesized, may be isolated from withinor around cells that produce them, or may be otherwise generated.

When antibodies are used as interaction partners, these may be preparedby any of a variety of techniques known to those of ordinary skill inthe art (e.g., see Harlow and Lane, Antibodies: A Laboratory Manual,Cold Spring Harbor Laboratory, 1988). For example, antibodies can beproduced by cell culture techniques, including the generation ofmonoclonal antibodies, or via transfection of antibody genes intosuitable bacterial or mammalian cell hosts, in order to allow for theproduction of recombinant antibodies. In one technique, an “immunogen”comprising an antigenic portion of a tumor marker of interest (or thetumor marker itself) is initially injected into any of a wide variety ofmammals (e.g., mice, rats, rabbits, sheep or goats). In this step, atumor marker (or an antigenic portion thereof) may serve as theimmunogen without modification. Alternatively, particularly forrelatively short tumor markers, a superior immune response may beelicited if the tumor marker is joined to a carrier protein, such asbovine serum albumin or keyhole limpet hemocyanin (KLH). The immunogenis injected into the animal host, preferably according to apredetermined schedule incorporating one or more booster immunizationsand the animals are bled periodically. Polyclonal antibodies specificfor the tumor marker may then be purified from such antisera by, forexample, affinity chromatography using the tumor marker (or an antigenicportion thereof) coupled to a suitable solid support. An exemplarymethod is described in Example 7.

If desired for diagnostic or therapeutic kits, monoclonal antibodiesspecific for a tumor marker of interest may be prepared, for example,using the technique of Kohler and Milstein, Eur. J. Immunol. 6:511, 1976and improvements thereto. Briefly, these methods involve the preparationof immortal cell lines capable of producing antibodies having thedesired specificity (i.e., reactivity with the tumor marker ofinterest). Such cell lines may be produced, for example, from spleencells obtained from an animal immunized as described above. The spleencells are then immortalized by, for example, fusion with a myeloma cellfusion partner, preferably one that is syngeneic with the immunizedanimal. A variety of fusion techniques may be employed. For example, thespleen cells and myeloma cells may be combined with a nonionic detergentfor a few minutes and then plated at low density on a selective mediumthat supports the growth of hybrid cells, but not myeloma cells. Apreferred selection technique uses HAT (hypoxanthine, aminopterin,thymidine) selection. After a sufficient time, usually about 1 to 2weeks, colonies of hybrids are observed. Single colonies are selectedand their culture supernatants tested for binding activity against thetumor marker. Hybridomas having high reactivity and specificity arepreferred.

Monoclonal antibodies may be isolated from the supernatants of growinghybridoma colonies. In addition, various techniques may be employed toenhance the yield, such as injection of the hybridoma cell line into theperitoneal cavity of a suitable vertebrate host, such as a mouse.Monoclonal antibodies may then be harvested from the ascites fluid orthe blood. Contaminants may be removed from the antibodies byconventional techniques, such as chromatography, gel filtration,precipitation and extraction. The tumor marker of interest may be usedin the purification process in, for example, an affinity chromatographystep.

In addition to inventive interaction partners, preferred kits for use inaccordance with the present invention may include, a reference sample,instructions for processing samples, performing the test, instructionsfor interpreting the results, buffers and/or other reagents necessaryfor performing the test. In certain embodiments the kit can comprise apanel of antibodies.

Pharmaceutical Compositions

As mentioned above, the present invention provides new therapies andmethods for identifying these. In certain embodiments, an interactionpartner may be a useful therapeutic agent. Alternatively oradditionally, interaction partners defined or prepared according to thepresent invention bind to tumor markers that serve as targets fortherapeutic agents. Also, inventive interaction partners may be used todeliver a therapeutic agent to a cancer cell. For example, interactionpartners provided in accordance with the present invention may becoupled to one or more therapeutic agents.

In addition, as mentioned above, to the extent that a particularpredictive panel correlates with responsiveness to a particular therapybecause it detects changes that reflect inhibition (or inhibitability)of cancer cell growth, that panel could be used to evaluate therapeuticcandidates (e.g., small molecule drugs) for their ability to induce thesame or similar changes in different cells. In particular, binding bythe panel could be assessed on cancer cells before and after exposure tocandidate therapeutics; those candidates that induce expression of thetumor markers to which the panel binds are then identified.

The invention includes pharmaceutical compositions comprising theseinventive therapeutic agents. In general, a pharmaceutical compositionwill include a therapeutic agent in addition to one or more inactiveagents such as a sterile, biocompatible carrier including, but notlimited to, sterile water, saline, buffered saline, or dextrosesolution. The pharmaceutical compositions may be administered eitheralone or in combination with other therapeutic agents including otherchemotherapeutic agents, hormones, vaccines and/or radiation therapy. By“in combination with”, it is not intended to imply that the agents mustbe administered at the same time or formulated for delivery together,although these methods of delivery are within the scope of theinvention. In general, each agent will be administered at a dose and ona time schedule determined for that agent. Additionally, the inventionencompasses the delivery of the inventive pharmaceutical compositions incombination with agents that may improve their bioavailability, reduceor modify their metabolism, inhibit their excretion, or modify theirdistribution within the body. The invention encompasses treating cancerby administering the pharmaceutical compositions of the invention.Although the pharmaceutical compositions of the present invention can beused for treatment of any subject (e.g., any animal) in need thereof,they are most preferably used in the treatment of humans.

The pharmaceutical compositions of this invention can be administered tohumans and other animals by a variety of routes including oral,intravenous, intramuscular, intra-arterial, subcutaneous,intraventricular, transdermal, rectal, intravaginal, intraperitoneal,topical (as by powders, ointments, or drops), bucal, or as an oral ornasal spray or aerosol. In general the most appropriate route ofadministration will depend upon a variety of factors including thenature of the agent (e.g., its stability in the environment of thegastrointestinal tract), the condition of the patient (e.g., whether thepatient is able to tolerate oral administration), etc. At present theintravenous route is most commonly used to deliver therapeuticantibodies. However, the invention encompasses the delivery of theinventive pharmaceutical composition by any appropriate route takinginto consideration likely advances in the sciences of drug delivery.

General considerations in the formulation and manufacture ofpharmaceutical agents may be found, for example, in Remington'sPharmaceutical Sciences, 19^(th) ed., Mack Publishing Co., Easton, Pa.,1995.

According to the methods of treatment of the present invention, canceris treated or prevented in a patient such as a human or other mammal byadministering to the patient a therapeutically effective amount of atherapeutic agent of the invention, in such amounts and for such time asis necessary to achieve the desired result. By a “therapeuticallyeffective amount” of a therapeutic agent of the invention is meant asufficient amount of the therapeutic agent to treat (e.g., to amelioratethe symptoms of, delay progression of, prevent recurrence of, cure,etc.) cancer at a reasonable benefit/risk ratio, which involves abalancing of the efficacy and toxicity of the therapeutic agent. Ingeneral, therapeutic efficacy and toxicity may be determined by standardpharmacological procedures in cell cultures or with experimentalanimals, e.g., by calculating the ED₅₀ (the dose that is therapeuticallyeffective in 50% of the treated subjects) and the LD₅₀ (the dose that islethal to 50% of treated subjects). The ED₅₀/LD₅₀ represents thetherapeutic index of the agent. Although in general therapeutic agentshaving a large therapeutic index are preferred, as is well known in theart, a smaller therapeutic index may be acceptable in the case of aserious disease, particularly in the absence of alternative therapeuticoptions. Ultimate selection of an appropriate range of doses foradministration to humans is determined in the course of clinical trials.

It will be understood that the total daily usage of the therapeuticagents and compositions of the present invention for any given patientwill be decided by the attending physician within the scope of soundmedical judgment. The specific therapeutically effective dose level forany particular patient will depend upon a variety of factors includingthe disorder being treated and the severity of the disorder; theactivity of the specific therapeutic agent employed; the specificcomposition employed; the age, body weight, general health, sex and dietof the patient; the time of administration, route of administration andrate of excretion of the specific therapeutic agent employed; theduration of the treatment; drugs used in combination or coincidentalwith the specific therapeutic agent employed; and like factors wellknown in the medical arts.

The total daily dose of the therapeutic agents of this inventionadministered to a human or other mammal in single or in divided dosescan be in amounts, for example, from 0.01 to 50 mg/kg body weight ormore usually from 0.1 to 25 mg/kg body weight. Single dose compositionsmay contain such amounts or submultiples thereof to make up the dailydose. In general, treatment regimens according to the present inventioncomprise administration to a patient in need of such treatment fromabout 0.1 μg to about 2000 mg of the therapeutic agent(s) of theinvention per day in single or multiple doses.

EXEMPLIFICATION Example 1 Selection of Candidate Genes andIdentification of Potential Interaction Partners for TumorClassification Panels

The present inventors identified a collection of candidate genes that(a) were differentially expressed across a set of tumor samples in amanner that suggested they distinguish biologically distinct classes oftumors; (b) were members of a gene functional class that has been linkedto cellular pathways implicated in tumor prognosis or drug resistance;(c) were known or thought to display an expression, localization,modification, or activity pattern that correlates with a relevant tumorfeature; etc.

For example, differentially expressed genes were identified usingmicroarrays as described in co-pending U.S. patent application Ser. No.09/916,722, filed Jul. 26, 2001 entitled “REAGENTS AND METHODS FOR USEIN MANAGING BREAST CANCER”, the entire contents of which areincorporated herein by reference. Other genes were typically selected onthe basis of published data suggesting their possible implication indrug resistance, cancer prognosis, etc. A total of 730 candidate geneswere identified as encoding proteins against which antibodies should beraised.

Rabbit polyclonal affinity-purified antibodies were then raised against661 of these proteins as described in Example 7. Each antibody wasinitially tested over a range of dilutions on tissue arrays thatincluded a set of normal tissues, tumor tissues and cell lines, so that,for each antibody, a discriminating titer was established at whichdifferential staining across the diverse set was observed. Thepreparation and staining of tissue arrays is described in greater detailin Example 8. Of the 661 antibodies subjected to this analysis, 460showed differential staining and were considered of sufficient interestfor further analysis.

Example 2 Breast Cancer Classification Panel (Russian Breast Cohort)

The present inventors prepared an exemplary panel of antibodies for usein classifying breast tumors. 272 of the 460 differentially stainingantibodies of Example 1 exhibited a reproducibly robust staining patternon tissues relevant for this application. These antibodies weretherefore applied (at appropriate titers) to a tissue array comprised ofapproximately 400 independent breast tumor samples from a cohort ofbreast cancer patients (the Russian breast cohort). Stained tissuesamples were scored by a trained cytotechnologist or pathologist on asemi-quantitative scale in which 0=no stain on tumor cells; 1=noinformation; 2=weak staining of tumor cells; and 3=strong staining oftumor cells. Antibodies were included in a breast cancer classificationpanel if they stained greater than 10% and less than 90% of a defined“consensus panel” of the breast tumor tissue samples on at least twoindependent tissue arrays.

A given tissue sample was included in this “consensus panel” if at least80% of the antibodies tested gave interpretable scores (i.e., a non-zeroscore) with that sample. Of the 400 breast tumor samples in the tissuearray about 320 were included in the consensus panel. Also, in scoringantibody binding to the consensus panel, all scores represented aconsensus score of replicate tissue arrays comprised of independentsamples from the same sources. The consensus score was determined bycomputing the median (rounded down to an integer, where applicable) ofall scores associated with a given antibody applied under identicalconditions to the particular patient sample. In cases where the varianceof the scores was greater than 2, the score was changed to 1 (i.e., noinformation). The data for each antibody was stored in an Oracle-baseddatabase that contained the semi-quantitative scores of tumor tissuestaining and also contained links to both patient clinical informationand stored images of the stained patient samples.

Through this analysis 90 of the 272 tested antibodies were selected forinclusion in an exemplary breast cancer classification panel (seeAppendix A, e.g., S0021, S0022, S0039, etc.). It is to be understoodthat any sub-combination of these 90 antibodies may be used inconstructing an inventive breast cancer classification panel. It willalso be appreciated that additional antibodies may be added to orremoved from an inventive breast cancer classification panel as moretumor markers are identified and/or more samples are tested (e.g., seeExample 3).

FIG. 1 shows the pattern of reactivity observed with certain members ofthis panel of antibodies across samples from the Russian breast cohort.Dark gray represents strong positive staining, black represents weakpositive staining, while light gray represents the absence of stainingand medium gray represents a lack of data. Images of stained samples canbe found in Appendix B (see right hand column of Appendix A forcross-references to corresponding antibodies).

The patients (rows) were classified using k-means clustering (asdescribed, for example, in MacQueen in Proceedings of the Fifth BerkeleySymposium on Mathematical Statistics and Probability (Le Cam et al.,Eds.; University of California Press, Berkeley, Calif.) 1:281, 1967;Heyer et al., Genome Res. 9:1106, 1999, each of which is incorporatedherein by reference) while the antibodies (columns) were organized usinghierarchical clustering (as described in, for example, Sokal et al.,Principles of Numerical Tazonomy (Freeman & Co., San Francisco, Calif.),1963; Eisen et al., Proc. Natl. Acad. Sci. USA 95:14863, 1998, each ofwhich is incorporated herein by reference). As shown in FIG. 1, ninesub-classes of breast cancer patients were identified by their consensuspattern of staining with this breast cancer classification panel.

Example 3 Breast Cancer Classification Panel (HH Breast Cohort)

In order to refine and expand the breast cancer classification panel ofExample 2, the present inventors tested 109 of the 460 differentiallystaining antibodies of Example 1 on samples from a new cohort of 550breast cancer patients (the Huntsville Hospital breast cohort or “HHbreast” cohort, the characteristics of which are described in Example10).

Antibodies were included in an updated breast cancer classificationpanel if they stained more than 10% and less than 90% of the particularconsensus panel of tissue samples tested. Through this analysis 87 ofthe 109 tested antibodies were selected (see Appendix A, e.g., S0011,S0018, S0020, etc.).

Example 4 Lung Cancer Classification Panel (Russian Lung Cohort)

The present inventors also prepared an exemplary panel of antibodies foruse in classifying lung tumors. 417 of the 460 differentially stainingantibodies of Example 1 exhibited a reproducibly robust staining patternon tissues relevant for this application. These antibodies weretherefore applied (at the titers determined in Example 1) to a tissuearray comprised of approximately 400 independent lung tumor tissues froma cohort of lung cancer patients (the Russian lung cohort). Stainedtissue samples were scored by a trained cytotechnologist or pathologistas before and again antibodies were included in the classification panelif they stained greater than 10% and less than 90% of a defined“consensus panel” of tissue samples on at least two independent tissuearrays.

Through this analysis an exemplary lung cancer classification panel wasgenerated that was made up of 106 of the 417 tested antibodies (seeAppendix A, e.g., s0021, s0022, s0024, etc.). It is to be understoodthat any sub-combination of these 106 antibodies may be used inconstructing an inventive lung cancer classification panel. It will alsobe appreciated that additional antibodies may be added to or removedfrom an inventive lung cancer classification panel as more tumor markersare identified and/or more samples are tested (e.g., see Example 5).

FIG. 2 shows the pattern of reactivity observed with certain members ofthis panel of antibodies across samples from the Russian lung cohort.Dark gray represents strong positive staining, black represents weakpositive staining, while light gray represents the absence of stainingand medium gray represents a lack of data. Images of stained samples canbe found in Appendix B (see right hand column of Appendix A forcross-references to corresponding antibodies).

The patients (rows) were again classified using k-means clustering whilethe antibodies (columns) were organized using hierarchical clustering.As shown in FIG. 2, eight sub-classes of lung cancer patients wereidentified by their consensus pattern of staining with this lung cancerclassification panel.

Example 5 Lung Cancer Classification Panel (HH Lung Cohort)

In order to refine and expand the lung cancer classification panel ofExample 4, the present inventors tested 54 of the 460 differentiallystaining antibodies of Example 1 on samples from a new cohort of 379lung cancer patients (the Huntsville Hospital lung cohort or “HH lung”cohort, the characteristics of which are described in Example 11).

Antibodies were included in an updated colon cancer classification panelif they stained more than 10% and less than 90% of the particularconsensus panel of tissue samples tested. Through this analysis 39 ofthe 54 tested antibodies were selected (see Appendix A, e.g., S0021,S0022, S0046, etc.).

Example 6 Colon Cancer Classification Panel (Russian Colon Cohort)

The present inventors also prepared an exemplary panel of antibodies foruse in classifying colon tumors. 382 of the 460 differentially stainingantibodies of Example 1 exhibited a reproducibly robust staining patternon tissues relevant for this application. These antibodies weretherefore applied (at the titers determined in Example 1) to a tissuearray comprised of approximately 400 independent colon tumor tissuesfrom a cohort of colon cancer patients (the Russian colon cohort).Stained tissue samples were scored by a trained cytotechnologist orpathologist as before and again antibodies were included in theclassification panel if they stained greater than 10% and less than 90%of a defined “consensus panel” of tissue samples on at least twoindependent tissue arrays.

Through this analysis a colon antibody classification panel wasgenerated that was made up of 86 of the 382 tested antibodies (seeAppendix A, e.g., S0022, S0036, S0039, etc.). It will be appreciatedthat any sub-combination of these 86 antibodies may be used inconstructing an inventive colon cancer classification panel. It willalso be appreciated that additional antibodies may be added to orremoved from an inventive colon cancer classification panel as moretumor markers are identified and/or more samples are tested.

FIG. 3 shows the pattern of reactivity observed with certain members ofthis panel of antibodies across samples from the Russian colon cohort.Dark gray represents strong positive staining, black represents weakpositive staining, while light gray represents the absence of stainingand medium gray represents a lack of data. Images of the stained samplescan be found in Appendix B (see right hand column of Appendix A forcross-references to corresponding antibodies).

The patients (rows) were again classified using k-means clustering whilethe antibodies (columns) were organized using hierarchical clustering.As shown in FIG. 3, seven sub-classes of patients were identified bytheir consensus pattern of staining with this exemplary colon cancerclassification panel.

Example 7 Raising Antibodies

This example describes a method that was employed to generate themajority of the antibodies that were used in Examples 1-6. Similarmethods may be used to generate an antibody that binds to anypolypeptide of interest (e.g., to polypeptides that are or are derivedfrom other tumor markers). In some cases, antibodies may be obtainedfrom commercial sources (e.g., Chemicon, Dako, Oncogene ResearchProducts, NeoMarkers, etc.) or other publicly available sources (e.g.,Imperial Cancer Research Technology, etc.).

Materials and Solutions

-   -   Anisole (Cat. No. A4405, Sigma, St. Louis, Mo.)    -   2,2′-azino-di-(3-ethyl-benzthiazoline-sulfonic acid) (ABTS)        (Cat. No. A6499, Molecular Probes, Eugene, Oreg.)    -   Activated maleimide Keyhole Limpet Hemocyanin (Cat. No. 77106,        Pierce, Rockford, Ill.)    -   Keyhole Limpet Hemocyanin (Cat. No. 77600, Pierce, Rockford,        Ill.)    -   Phosphoric Acid (H₃PO₄) (Cat. No. P6560, Sigma)    -   Glacial Acetic Acid (Cat No. BP1185-500, Fisher)    -   EDC (EDAC) (Cat No. 341006, Calbiochem)    -   25% Glutaraldehyde (Cat No. G-5882, Sigma)    -   Glycine (Cat No. G-8898, Sigma)    -   Biotin (Cat. No. B2643, Sigma)    -   Boric acid (Cat. No. B0252, Sigma)    -   Sepharose 4B (Cat. No. 17-0120-01, LKB/Pharmacia, Uppsala,        Sweden)    -   Bovine Serum Albumin (LP) (Cat. No. 100 350, Boehringer        Mannheim, Indianapolis, Ind.)    -   Cyanogen bromide (Cat. No. C6388, Sigma)    -   Dialysis tubing Spectra/Por Membrane MWCO: 6-8,000 (Cat. No. 132        665, Spectrum Industries, Laguna Hills, Calif.)    -   Dimethyl formamide (DMF) (Cat. No. 22705-6, Aldrich, Milwaukee,        Wis.)    -   DIC (Cat. No. BP 592-500, Fisher)    -   Ethanedithiol (Cat. No. 39, 802-0, Aldrich)    -   Ether (Cat. No. TX 1275-3, EM Sciences)    -   Ethylenediaminetetraacetatic acid (EDTA) (Cat. No. BP 120-1,        Fisher, Springfield, N.J.)    -   1-ethyl-3-(3′ dimethylaminopropyl)-carbodiimide, HCL (EDC) (Cat.        no. 341-006, Calbiochem, San Diego, Calif.)    -   Freund's Adjuvant, complete (Cat. No. M-0638-50B, Lee        Laboratories, Grayson, Ga.)    -   Freund's Adjuvant, incomplete (Cat. No. M-0639-50B, Lee        Laboratories)    -   Fritted chromatography columns (Column part No. 12131011; Frit        Part No. 12131029, Varian Sample Preparation Products, Harbor        City, Calif.)    -   Gelatin from Bovine Skin (Cat. No. G9382, Sigma)    -   Goat anti-rabbit IgG, biotinylated (Cat. No. A 0418, Sigma)    -   HOBt (Cat. No. 01-62-0008, Calbiochem)    -   Horseradish peroxidase (HRP) (Cat. No. 814 393, Boehringer        Mannheim)    -   HRP-Streptavidin (Cat. No. S 5512, Sigma)    -   Hydrochloric Acid (Cat. No. 71445-500, Fisher)    -   Hydrogen Peroxide 30% w/w (Cat. No. H1009, Sigma)    -   Methanol (Cat. No. A412-20, Fisher)    -   Microtiter plates, 96 well (Cat. No. 2595, Corning-Costar,        Pleasanton, Calif.)    -   N-α-Fmoc protected amino acids from Calbiochem. See '97-'98        Catalog pp. 1-45.    -   N-α-Fmoc protected amino acids attached to Wang Resin from        Calbiochem. See '97-'98 Catalog pp. 161-164.    -   NMP (Cat. No. CAS 872-50-4, Burdick and Jackson, Muskegon,        Mich.)    -   Peptide (Synthesized by Research Genetics. Details given below)    -   Piperidine (Cat. No. 80640, Fluka, available through Sigma)    -   Sodium Bicarbonate (Cat. No. BP328-1, Fisher)    -   Sodium Borate (Cat. No. B9876, Sigma)    -   Sodium Carbonate (Cat. No. BP357-1, Fisher)    -   Sodium Chloride (Cat. No. BP 358-10, Fisher)    -   Sodium Hydroxide (Cat. No. SS 255-1, Fisher)    -   Streptavidin (Cat. No. 1 520, Boehringer Mannheim)    -   Thioanisole (Cat. No. T-2765, Sigma)    -   Trifluoroacetic acid (Cat. No. TX 1275-3, EM Sciences)    -   Tween-20 (Cat. No. BP 337-500, Fisher)    -   Wetbox (Rectangular Servin' Saver™ Part No. 3862, Rubbermaid,        Wooster, Ohio)    -   BBS—Borate Buffered Saline with EDTA dissolved in distilled        water (pH 8.2 to 8.4 with HCl or NaOH), 25 mM Sodium borate        (Borax), 100 mM Boric Acid, 75 mM NaCl and 5 mM EDTA.    -   0.1 N HCl in saline as follows: concentrated HCl (8.3 ml/0.917        liter distilled water) and 0.154 M NaCl    -   Glycine (pH 2.0 and pH 3.0) dissolved in distilled water and        adjusted to the desired pH, 0.1 M glycine and 0.154 M NaCl.    -   5× Borate 1× Sodium Chloride dissolved in distilled water, 0.11        M NaCl, 60 mM Sodium Borate and 250 mM Boric Acid.    -   Substrate Buffer in distilled water adjusted to pH 4.0 with        sodium hydroxide, 50 to 100 mM Citric Acid.    -   AA solution: HOBt is dissolved in NMP (8.8 grams HOBt to 1 liter        NMP). Fmoc-N-a-amino at a concentration at 0.53 M.    -   DIC solution: 1 part DIC to 3 parts NMP.    -   Deprotecting solution: 1 part Piperidine to 3 parts DMF.    -   Reagent R: 2 parts anisole, 3 parts ethanedithiol, 5 parts        thioanisole and 90 parts trifluoroacetic acid.

Equipment

-   -   MRX Plate Reader (Dynatech, Chantilly, Va.)    -   Hamilton Eclipse (Hamilton Instruments, Reno, Nev.)    -   Beckman TJ-6 Centrifuge (Model No. TJ-6, Beckman Instruments,        Fullerton, Calif.)    -   Chart Recorder (Recorder 1 Part No. 18-1001-40, Pharmacia LKB        Biotechnology)    -   UV Monitor (Uvicord SU Part No. 18-1004-50, Pharmacia LKB        Biotechnology)    -   Amicon Stirred Cell Concentrator (Model 8400, Amicon, Beverly,        Mass.)    -   30 kD MW cut-off filter (Cat. No. YM-30 Membranes Cat. No.        13742, Amicon)    -   Multi-channel Automated Pipettor (Cat. No. 4880, Corning Costar,        Cambridge, Mass.)    -   pH Meter Corning 240 (Corning Science Products, Corning        Glassworks, Corning, N.Y.)    -   ACT396 peptide synthesizer (Advanced ChemTech, Louisville, Ky.)    -   Vacuum dryer (Box from Labconco, Kansas City, Mo. and Pump from        Alcatel, Laurel, Md.).    -   Lyophilizer (Unitop 600s1 in tandem with Freezemobile 12, both        from Virtis, Gardiner, N.Y.)

Peptide Selection

Peptides against which antibodies would be raised were selected fromwithin the polypeptide sequence of interest using a program that usesthe Hopp/Woods method (described in Hopp and Woods, Mol. Immunol.20:483, 1983 and Hopp and Woods, Proc. Nat. Acad. Sci. U.S.A. 78:3824,1981). The program uses a scanning window that identifies peptidesequences of 15-20 amino acids containing several putative antigenicepitopes as predicted by low solvent accessibility. This is in contrastto most implementations of the Hopp/Woods method, which identify singleshort (˜6 amino acids) presumptive antigenic epitopes. Occasionally thepredicted solvent accessibility was further assessed by PHD predictionof loop structures (described in Rost and Sander, Proteins 20:216,1994). Preferred peptide sequences display minimal similarity withadditional known human proteins. Similarity was determined by performingBLASTP alignments, using a wordsize of 2 (described in Altschul et al.,J. Mol. Biol. 215:403, 1990). All alignments given an EXPECT value lessthan 1000 were examined and alignments with similarities of greater than60% or more than four residues in an exact contiguous non-gappedalignment forced those peptides to be rejected. When it was desired totarget regions of proteins exposed outside the cell membrane,extracellular regions of the protein of interest were determined fromthe literature or as defined by predicted transmembrane domains using ahidden Markov model (described in Krogh et al., J. Mol. Biol. 305:567,2001). When the peptide sequence was in an extracellular domain,peptides were rejected if they contained N-linked glycosylation sites.As shown in Appendix A, one to three peptide sequences were selected foreach polypeptide using this procedure.

Peptide Synthesis

The sequence of the desired peptide was provided to the peptidesynthesizer. The C-terminal residue was determined and the appropriateWang Resin was attached to the reaction vessel. The peptides weresynthesized C-terminus to N-terminus by adding one amino acid at a timeusing a synthesis cycle. Which amino acid is added was controlled by thepeptide synthesizer, which looks to the sequence of the peptide that wasentered into its database. The synthesis steps were performed asfollows:

-   -   Step 1—Resin Swelling: Added 2 ml DMF, incubated 30 minutes,        drained DMF.    -   Step 2—Synthesis cycle (repeated over the length of the peptide)        -   2a—Deprotection: 1 ml deprotecting solution was added to the            reaction vessel and incubated for 20 minutes.        -   2b—Wash Cycle        -   2c—Coupling: 750 ml of amino acid solution (changed as the            sequence listed in the peptide synthesizer dictated) and 250            ml of DIC solution were added to the reaction vessel. The            reaction vessel was incubated for thirty minutes and washed            once. The coupling step was repeated once.        -   2d—Wash Cycle    -   Step 3—Final Deprotection: Steps 2a and 2b were performed one        last time.

Resins were deswelled in methanol (rinsed twice in 5 ml methanol,incubated 5 minutes in 5 ml methanol, rinsed in 5 ml methanol) and thenvacuum dried.

Peptide was removed from the resin by incubating 2 hours in reagent Rand then precipitated into ether. Peptide was washed in ether and thenvacuum dried. Peptide was resolubilized in diH₂O, frozen and lyophilizedovernight.

Conjugation of Peptide with Keyhole Limpet Hemocyanin

Peptide (6 mg) was conjugated with Keyhole Limpet Hemocyanin (KLH). Whenthe selected peptide included at least one cysteine, three aliquots (2mg) were dissolved in PBS (2 ml) and coupled to KLH via glutaraldehyde,EDC or maleimide activated KLH (2 mg) in 2 ml of PBS for a total volumeof 4 ml. When the peptide lacked cysteine, two aliquots (3 mg) werecoupled via glutaraldehyde and EDC methods.

Maleimide coupling is accomplished by mixing 2 mg of peptide with 2 mgof maleimide-activated KLH dissolved in PBS (4 ml) and incubating 4 hr.

EDC coupling is accomplished by mixing 2 mg of peptide, 2 mg unmodifiedKLH, and 20 mg of EDC in 4 ml PBS (lowered to pH 5 by the addition ofphosphoric acid), and incubating for 4 hours. The reaction is stopped bythe slow addition of 1.33 ml acetic acid (pH 4.2). When using EDC tocouple 3 mg of peptide, the amounts listed above are increased by afactor of 1.5.

Glutaraldehyde coupling occurs when 2 mg of peptide are mixed with 2 mgof KLH in 0.9 ml of PBS. 0.9 ml of 0.2% glutaraldehyde in PBS is addedand mixed for one hour. 0.46 ml of 1 M glycine in PBS is added and mixedfor one hour. When using glutaraldehyde to couple 3 mg of peptide, theabove amounts are increased by a factor of 1.5.

The conjugated aliquots were subsequently repooled, mixed for two hours,dialyzed in 1 liter PBS and lyophilized.

Immunization of Rabbits

Two New Zealand White Rabbits were injected with 250 μg (total) KLHconjugated peptide in an equal volume of complete Freund's adjuvant andsaline in a total volume of 1 ml. 100 μg KLH conjugated peptide in anequal volume of incomplete Freund's Adjuvant and saline were theninjected into three to four subcutaneous dorsal sites for a total volumeof 1 ml two, six, eight and twelve weeks after the first immunization.The immunization schedule was as follows:

Day 0 Pre-immune bleed, primary immunization Day 15 1st boost Day 27 1stbleed Day 44 2nd boost Day 57 2nd bleed and 3rd boost Day 69 3rd bleedDay 84 4th boost Day 98 4th bleed

Collection of Rabbit Serum

The rabbits were bled (30 to 50 ml) from the auricular artery. The bloodwas allowed to clot at room temperature for 15 minutes and the serum wasseparated from the clot using an IEC DPR-6000 centrifuge at 5000 g.Cell-free serum was decanted gently into a clean test tube and stored at−20° C. for affinity purification.

Determination of Antibody Titer

All solutions with the exception of wash solution were added by theHamilton Eclipse, a liquid handling dispenser. The antibody titer wasdetermined in the rabbits using an ELISA assay with peptide on the solidphase. Flexible high binding ELISA plates were passively coated withpeptide diluted in BBS (100 μl, 1 μg/well) and the plate was incubatedat 4° C. in a wetbox overnight (air-tight container with moistenedcotton balls). The plates were emptied and then washed three times withBBS containing 0.1% Tween-20 (BBS-TW) by repeated filling and emptyingusing a semi-automated plate washer. The plates were blocked bycompletely filling each well with BBS-TW containing 1% BSA and 0.1%gelatin (BBS-TW-BG) and incubating for 2 hours at room temperature. Theplates were emptied and sera of both pre- and post-immune serum wereadded to wells. The first well contained sera at 1:50 in BBS. The serawere then serially titrated eleven more times across the plate at aratio of 1:1 for a final (twelfth) dilution of 1:204,800. The plateswere incubated overnight at 4° C. The plates were emptied and washedthree times as described.

Biotinylated goat anti-rabbit IgG (100 μl) was added to each microtiterplate test well and incubated for four hours at room temperature. Theplates were emptied and washed three times. Horseradishperoxidase-conjugated Streptavidin (100 μl diluted 1:10,000 inBBS-TW-BG) was added to each well and incubated for two hours at roomtemperature. The plates were emptied and washed three times. The ABTSwas prepared fresh from stock by combining 10 ml of citrate buffer (0.1M at pH 4.0), 0.2 ml of the stock solution (15 mg/ml in water) and 10 μlof 30% hydrogen peroxide. The ABTS solution (100 μl) was added to eachwell and incubated at room temperature. The plates were read at 414 nm,20 minutes following the addition of substrate.

Preparation of Peptide Affinity Purification Column:

The affinity column was prepared by conjugating 5 mg of peptide to 10 mlof cyanogen bromide-activated Sepharose 4B and 5 mg of peptide tohydrazine-Sepharose 4B. Briefly, 100 μl of DMF was added to peptide (5mg) and the mixture was vortexed until the contents were completelywetted. Water was then added (900 μl) and the contents were vortexeduntil the peptide dissolved. Half of the dissolved peptide (500 μl) wasadded to separate tubes containing 10 ml of cyanogen-bromide activatedSepharose 4B in 0.1 ml of borate buffered saline at pH 8.4 (BBS) and 10ml of hydrazine-Sepharose 4B in 0.1 M carbonate buffer adjusted to pH4.5 using excess EDC in citrate buffer pH 6.0. The conjugation reactionswere allowed to proceed overnight at room temperature. The conjugatedSepharose was pooled and loaded onto fritted columns, washed with 10 mlof BBS, blocked with 10 ml of 1 M glycine and washed with 10 ml 0.1 Mglycine adjusted to pH 2.5 with HCl and re-neutralized in BBS. Thecolumn was washed with enough volume for the optical density at 280 nmto reach baseline.

Affinity Purification of Antibodies

The peptide affinity column was attached to a UV monitor and chartrecorder. The titered rabbit antiserum was thawed and pooled. The serumwas diluted with one volume of BBS and allowed to flow through thecolumns at 10 ml per minute. The non-peptide immunoglobulins and otherproteins were washed from the column with excess BBS until the opticaldensity at 280 nm reached baseline. The columns were disconnected andthe affinity purified column was eluted using a stepwise pH gradientfrom pH 7.0 to 1.0. The elution was monitored at 280 nm and fractionscontaining antibody (pH 3.0 to 1.0) were collected directly into excess0.5 M BBS. Excess buffer (0.5 M BBS) in the collection tubes served toneutralize the antibodies collected in the acidic fractions of the pHgradient.

The entire procedure was repeated with “depleted” serum to ensuremaximal recovery of antibodies. The eluted material was concentratedusing a stirred cell apparatus and a membrane with a molecular weightcutoff of 30 kD. The concentration of the final preparation wasdetermined using an optical density reading at 280 nm. The concentrationwas determined using the following formula: mg/ml=OD₂₈₀/1.4.

It will be appreciated that in certain embodiments, additional steps maybe used to purify antibodies of the invention. In particular, it mayprove advantageous to repurify antibodies, e.g., against one of thepeptides that was used in generating the antibodies. It is to beunderstood that the present invention encompasses antibodies that havebeen prepared with such additional purification or repurification steps.It will also be appreciated that the purification process may affect thebinding between samples and the inventive antibodies.

Example 8 Preparing and Staining Tissue Arrays

This example describes a method that was employed to prepare the tissuearrays that were used in Examples 1-6. This example also describes howthe antibody staining was performed.

Tissue arrays were prepared by inserting full-thickness cores from alarge number of paraffin blocks (donor blocks) that contain fragments oftissue derived from many different patients and/or different tissues orfragments of tissues from a single patient, into a virgin paraffin block(recipient block) in a grid pattern at designated locations in a grid. Astandard slide of the paraffin embedded tissue (donor block) was thenmade which contained a thin section of the specimen amenable to H & Estaining A trained pathologist, or the equivalent versed in evaluatingtumor and normal tissue, designated the region of interest for samplingon the tissue array (e.g., a tumor area as opposed to stroma). Acommercially available tissue arrayer from Beecher Instruments was thenused to remove a core from the donor block which was then inserted intothe recipient block at a designated location. The process was repeateduntil all donor blocks had been inserted into the recipient block. Therecipient block was then thin-sectioned to yield 50-300 slidescontaining cores from all cases inserted into the block.

The selected antibodies were then used to perform immunohistochemicalstaining using the DAKO Envision+, Peroxidase IHC kit (DAKO Corp.,Carpenteria, Calif.) with DAB substrate according to the manufacturer'sinstructions.

Example 9 Correlating Interaction Partner Binding withOutcome/Responsiveness of Xenograft Tumors

According to the present invention, panels of useful interactionpartners may be defined through analysis of human tumor cells grown in anon-human host. In particular, such analyses may define interactionpartner panels whose binding correlates with prognosis and/or withresponsiveness to therapy.

Cells derived from human tumors may be transplanted into a host animal(e.g., a mouse), preferably into an immunocompromised host animal. Inpreferred embodiments of the invention, cells (e.g., cell lines, tumorsamples obtained from human patients, etc.) from a variety of differenthuman tumors (e.g., at least 10, 20, 30, 40, 50, 60 or more differenttumors) are transplanted into host animals. The animals are then treatedwith different (e.g., increasing) concentrations of a chemical compoundknown or thought to be selectively toxic to tumors with a predeterminedcommon characteristic (e.g., class or subclass). Relative growth orregression of the tumors may then be assessed using standard techniques.

In certain embodiments of the invention, a dataset of sensitivity of thetransplanted cells to a given compound or set of compounds mayoptionally be created. For example, a dataset might consist of theconcentration of compound administered to the host animal that inhibitedtumor growth 50% at 96 hr (i.e., the LD₅₀) for each of the cell samplesor cell lines tested. Such a dataset, for example across at least 10,20, 30, 40, 50, 60 or more cell lines, could then be correlated with therelative staining of the binding partners across the same cell lines.Those binding partners whose interaction (or lack thereof) with cellswas highly correlated with either sensitivity to or resistance to agiven compound would be useful members of a predictive panel.

Example 10 Correlating Interaction Partner Binding with ClinicalPrognostic Data in Breast Cancer

According to the present invention, panels of useful interactionpartners may be defined through analysis of correlations between bindingpatterns and clinical prognostic data. In particular, such analyses maydefine interaction partner panels whose binding correlates withprognosis.

The following describes the identification of exemplary panels ofantibodies whose binding has been shown to correlate with the prognosisof breast cancer patients. The data was obtained using samples from theHuntsville Hospital breast cohort (the “HH breast” cohort) that wasreferred to in Example 3.

The HH breast cohort was generated from 1082 breast cancer patients thatwere treated by the Comprehensive Cancer Institute (Huntsville, Ala.)between 1990 and 2000. This larger group was filtered to a study groupof 550 patients by eliminating patients according to the followingcriteria: 249 that had no chart which could be found; 103 that had noclinical follow up; and 180 that did not have sufficient clinicalmaterial in the paraffin block to sample. For the remaining 550patients, clinical data through Dec. 31, 2002 was available. Everypatient in the cohort therefore had between 2 and 13 years of follow-up.The average time of follow-up among patients who did not recur was 5.6years. Of the 550 patients, 140 had a recurrence of cancer within thestudy period; 353 patients were estrogen receptor positive (ER+); 154were estrogen receptor negative (ER−); and 43 were undetermined. Somepatients within these groups received adjuvant hormone therapy as shownin Table 1:

TABLE 1 Total Hormone No hormone Unknown ER+ 353 278 68 7 ER− 154 70 831 Undetermined 43 28 15 0

In addition, 263 patients received chemotherapy. Up to 16 differentregimens were used, however, most were variants of cyclophosphamide,doxorubicin (with and without 5-fluorouracil and/or cyclophosphamide),methotrexate and 5-fluorouracil. Finally, 333 of the patients receivedradiation. Clinical information regarding age, stage, node status, tumorsize, and grade was obtained.

The clinical information for the patients in the cohort is summarized inTable 2.

TABLE 2 All (550) ER+ (353) ER− (154) Stage = 1 236 162 49 Stage = 2 269167 87 Stage = 3 44 23 18 Undetermined 1 0 0 Mean Age @ Dx 58 59 55Tumor status = 0 1 0 1 Tumor status = 1 295 203 63 Tumor status = 2 195122 62 Tumor status = 3 26 14 11 Tumor status = 4 14 6 8 Undetermined 218 9 Node status = 0 326 215 76 Node status = 1 205 127 71 Node status =2 10 6 3 Undetermined 10 5 4 Metastasis = 0 527 338 147 Metastasis = 1 54 1 Undetermined 19 11 6

Where each category is defined in Table 3. These rules are not fixed andstaging is typically done by an oncologist based on TNM status and otherfactors. These definitions for staging will not necessarily match withthe stage that each patient was actually given. Node status is theprimary tool for staging purposes.

TABLE 3 Tumor status = 0 No evidence of tumor Tumor status = 1  <2 cmTumor status = 2 2-5 cm Tumor status = 3  >5 cm Tumor status = 4 Anysize but extends to chest wall Node status = 0 No regional LN metastasisNode status = 1 Ancillary LN metastasis but nodes still moveable Nodestatus = 2 Ancillary LN metastasis with nodes fixed to each other ORinternal mammary node metastasis Metastasis = 0 No distant metastasisMetastasis = 1 Distant metastasis Stage = 1 T1, N0, M0 Stage = 2 T0, N1,M0 T1, N1, M0 T2, N0, M0 T2, N1, M0 T3, N0, M0 Stage = 3 T(0-3), N2, M0T3, N1, M0 T4, NX, M0 Stage = 4 TX, NX, M1

Samples from patients in the cohort were stained with antibodies fromthe breast cancer classification panel identified in Appendix A (aspreviously described in Examples 2 and 3). The stained samples were thenscored in a semi-quantitative fashion, with 0=negative, 1=weak staining,and 2=strong staining When appropriate, alternative scoring systems wereused (i.e., 0=negative, 1=weak or strong; or 0=negative or weak and1=strong staining) For each antibody, the scoring system used wasselected to produce the most significant prognostication of thepatients, as determined by a log-rank test (e.g., see Mantel andHaenszel, Journal of the National Cancer Institute 22:719-748, 1959).The results are presented in Appendix C and are grouped into fourcategories that have been clinically recognized to be of significance:all patients, ER+ patients, ER− patients, and ER+/node− patients. Asshown, the antibodies were found to have differing significances foreach of these categories of breast cancer patients.

It is to be understood that exclusion of a particular antibody from anyprognostic panel based on these experiments is not determinative.Indeed, it is anticipated that additional data with other samples maylead to the identification of other antibodies (from Appendix A andbeyond) that may have prognostic value for these and other classes ofpatients.

The expected relationship between the staining of patient samples witheach antibody and the recurrence of tumors was measured using theKaplan-Meier estimate of expected recurrence (e.g., see Kaplan andMeier, J. Am. Stat. Assn. 53:457-81, 1958). The log-rank test was usedto determine the significance of different expected recurrences for eachantibody (e.g., see Mantel and Haenszel, Journal of the National CancerInstitute, 22:719-748, 1959). This produces the p-value that is listedfor each antibody in Appendix C. Preferred antibodies are those thatproduce a p-value of less than 0.10.

The degree to which these antibodies predicted recurrence was determinedusing a Cox univariate proportional hazard model (e.g., see Cox andOakes, “Analysis of Survival Data”, Chapman & Hall, 1984). The “hazardratio” listed in Appendix C for each antibody reflects the predictedincrease in risk of recurrence for each increase in the staining score.Scores greater than 1.0 indicate that staining predicts an increasedrisk of recurrence compared to an average individual, scores less than1.0 indicate that staining predicts a decreased risk.

It will be appreciated that these antibodies can be used alone or incombinations to predict recurrence (e.g., in combinations of 2, 3, 4, 5,6, 7, 8, 9, 10 or more antibodies). It will also be appreciated thatwhile a given antibody may not predict recurrence when used alone thesame antibody may predict recurrence when used in combination withothers. It will also be understood that while a given antibody orcombination of antibodies may not predict recurrence in a given set ofpatients (e.g., ER+ patients), the same antibody or combination ofantibodies may predict recurrence in a different set of patients (e.g.,ER− patients). Similarly, it is to be understood that while a givenantibody or combination of antibodies may not predict recurrence in agiven set of patients (e.g., ER+ patients), the same antibody orcombination of antibodies may predict recurrence in a subset of thesepatients (e.g., ER+/node negative patients).

These prognostic panels could be constructed using any method. Withoutlimitation these include simple empirically derived rules, Coxmultivariate proportional hazard models (e.g., see Cox and Oakes,“Analysis of Survival Data”, Chapman & Hall, 1984), regression trees(e.g., see Segal and Bloch, Stat. Med. 8:539-50, 1989), and/or neuralnetworks (e.g., see Ravdin et al., Breast Cancer Res. Treat. 21:47-53,1992). In certain embodiments a prognostic panel might include between2-10 antibodies, for example 3-9 or 5-7 antibodies. It will beappreciated that these ranges are exemplary and non-limiting.

The prognostic value of exemplary panels of antibodies were alsoassessed by generating Kaplan-Meier recurrence curves for ER+ andER+/node− patients and then comparing these with curves produced forthese same patients with the standard Nottingham Prognostic Index (NPI).

In order to generate Kaplan-Meier curves based on antibody panels, Coxunivariate proportional hazard regression models were first run with allantibodies from Appendix C utilizing all three scoring procedures. Theantibodies and scoring systems best able to predict recurrence were thenused in a regression tree model and pruned to maintain predictive powerwhile reducing complexity. Patients whom the panel predicted as beingstrongly likely to recur were placed in the “poor” prognosis group.Patients whom the panel predicted as being strongly unlikely to recurwere given the prediction of “good”. Patients whom the panel predictedas neither being strongly likely to recur or not recur were placed inthe “moderate” prognosis group. Kaplan-Meier curves were then calculatedbased on recurrence data for patients within each group. FIG. 4A showthe curves that were obtained for ER+ patients in each of theseprognostic groups. FIG. 5A show the curves that were obtained forER+/node− patients in each of these prognostic groups.

The antibodies from Appendix C that were used to predict recurrence forER+ patients (FIG. 4A) were: s0296P1 (1:225 dilution, scoring method 3),s6006 (1:1 dilution, scoring method 2), s0545 (1:900 dilution, scoringmethod 2), s0063 (1:300 dilution, scoring method 2), s6002 (1:1dilution, scoring method 3), s0081 (1:20 dilution, scoring method 2),s0255 (1:1000 dilution, scoring method 3), and s0039 (1:100 dilution,scoring method 2).

The antibodies from Appendix C that were used to predict recurrence forER+/node− patients (FIG. 5A) were: s0143P3 (1:630 dilution, scoringmethod 1), s0137 (1:2500 dilution, scoring method 2), s0260 (1:5400dilution, scoring method 2), s0702 (1:178200 dilution, scoring method2), s0545 (1:900 dilution, scoring method 2), s6002 (1:1 dilution,scoring method 1), s6007 (1:1 dilution, scoring method 1).

Kaplan-Meier recurrence curves were then generated for the same patientsbased on their standard NPI scores. NPI scores were calculated forpatients according to the standard formula NPI=(0.2×tumor diameter incm)+lymph node stage+tumor grade. As is well known in the art, lymphnode stage is either 1 (if there are no nodes affected), 2 (if 1-3glands are affected) or 3 (if more than 3 glands are affected). Thetumor grade was scored according to the Bloom-Richardson Grade system(Bloom and Richardson, Br. J. Cancer 11:359-377, 1957). According tothis system, tumors were examined histologically and given a score forthe frequency of cell mitosis (rate of cell division), tubule formation(percentage of cancer composed of tubular structures), and nuclearpleomorphism (change in cell size and uniformity). Each of thesefeatures was assigned a score ranging from 1 to 3 as shown in Table 4.The scores for each feature were then added together for a final sumthat ranged between 3 to 9. A tumor with a final sum of 3, 4, or 5 wasconsidered a Grade 1 tumor (less aggressive appearance); a sum of 6 or 7a Grade 2 tumor (intermediate appearance); and a sum of 8 or 9 a Grade 3tumor (more aggressive appearance).

TABLE 4 Score Tubule formation (% of carcinoma composed of tubularstructures) >75% 1 10-75% 2 <10% 3 Nuclear pleomorphism (Change inCells) Small, uniform cells 1 Moderate increase in size and 2 variationMarked variation 3 Mitosis Count (Cell Division) Up to 7 1 8 to 14 2 15or more 3

Patients with tumors having an overall NPI score of less than 3.4 wereplaced in the “good” prognosis group. Those with an NPI score of between3.4 and 5.4 were placed in the “moderate” prognosis group and patientswith an NPI score of more than 5.4 were placed in the “poor” prognosisgroup. Kaplan-Meier curves were then calculated based on recurrence datafor patients within each group. FIG. 4B show the curves that wereobtained for ER+ patients in each of these NPI prognostic groups. FIG.5B show the curves that were obtained for ER+/node− patients in each ofthese NPI prognostic groups. By definition ER+/node− patients have anNPI score that is less than 5.4. This explains why there is no “poor”prognosis curve in FIG. 5B. Example 12 describes other exemplaryprognostic panels for breast cancer patients.

Example 11 Correlating Interaction Partner Binding with ClinicalPrognostic Data in Lung Cancer

This Example describes the identification of exemplary panels ofantibodies whose binding has been shown to correlate with the prognosisof lung cancer patients. The data was obtained using samples from theHuntsville Hospital lung cohort (the “HH lung” cohort) that was referredto in Example 5.

The HH lung cohort was generated from 544 lung cancer patients that weretreated by the Comprehensive Cancer Institute (Huntsville, Ala.) between1987 and 2002. This larger group was filtered to a study group of 379patients by eliminating patients that had insufficient clinical followup or that did not have sufficient clinical material in the paraffinblock to sample. For the remaining patients, clinical data through Sep.30, 2003 was available. This set of patients consisted of 232 males and147 females. The average time of follow-up among patients who did notrecur was 3.5 years. Of the 379 patients, 103 had a recurrence of cancerwithin the study period. All patients in this study were diagnosed at apathological stage of 1 or 2, with 305 patients at stage 1, 1A, or 1B,and 74 patients at stage 2, 2A, or 2B.

Samples from patients in the cohort were stained with antibodies fromthe lung cancer classification panel identified in Appendix A (aspreviously described in Examples 4 and 5). The stained samples were thenscored in a semi-quantitative fashion; scoring methods 1-3 use thefollowing schemes: method 1 (0=negative; 1=weak; 2=strong); method 2(0=negative; 1=weak or strong); and method 3 (0=negative or weak;1=strong). For each antibody, the scoring system used was selected toproduce the most significant prognostication of the patients, asdetermined by a log-rank test (e.g., see Mantel and Haenszel, Journal ofthe National Cancer Institute 22:719-748, 1959). The results arepresented in Appendix D and are grouped into three categories that havebeen clinically recognized to be of significance: all patients,adenocarcinoma patients, and squamous cell carcinoma patients. As shown,the antibodies were found to have differing significances for each ofthese categories of lung cancer patients.

It is to be understood that exclusion of a particular antibody from anyprognostic panel based on these experiments is not determinative.Indeed, it is anticipated that additional data with other samples maylead to the identification of other antibodies (from Appendix A andbeyond) that may have prognostic value for these and other classes ofpatients.

As for the breast study of Example 10, the expected relationship betweenthe staining of patient samples with each antibody and the recurrence oftumors was measured using the Kaplan-Meier estimate of expectedrecurrence and a log-rank test was used to determine the significance ofdifferent expected recurrences. This produces the p-value that is listedfor each antibody in Appendix D. Preferred antibodies are those thatproduce a p-value of less than 0.10.

The degree to which these antibodies predicted recurrence was determinedusing a Cox univariate proportional hazard model. The “hazard ratio”listed in Appendix D for each antibody reflects the predicted increasein risk of recurrence for each increase in the staining score. Scoresgreater than 1.0 indicate that staining predicts an increased risk ofrecurrence compared to an average individual, scores less than 1.0indicate that staining predicts a decreased risk.

As a number of patients had information regarding whether or not thecancer recurred but lacked information on time to recurrence, achi-square test was also performed. This standard statistical test showsthe degree of divergence between observed and expected frequencies anddoes not employ time to recurrence, as does the log-rank test. Preferredantibodies are those that produce a p-value of less than 0.10.

It will be appreciated that these prognostic antibodies can be usedalone or in combinations to predict recurrence (e.g., in combinations of2, 3, 4, 5, 6, 7, 8, 9, 10 or more antibodies). It will also beappreciated that while a given antibody may not predict recurrence whenused alone, the same antibody may predict recurrence when used incombination with others. It will also be understood that while a givenantibody or combination of antibodies may not predict recurrence in agiven set of patients (e.g., adenocarcinoma patients), the same antibodyor combination of antibodies may predict recurrence in a different setof patients (e.g., squamous cell carcinoma patients).

As for the breast study of Example 10, these prognostic panels could beconstructed using any method. Without limitation these include simpleempirically derived rules, Cox multivariate proportional hazard models,regression trees, and/or neural networks. In certain embodiments aprognostic panel might include between 2-10 antibodies, for example 3-9or 5-7 antibodies. It will be appreciated that these ranges areexemplary and non-limiting. The construction of exemplary prognosticpanels for lung cancer patients is described in Example 13.

Example 12 Prognostic Breast Cancer Panels

This Example builds on the results of Example 10 and describes theidentification of additional exemplary panels of antibodies whosebinding has been shown to correlate with the prognosis of breast cancerpatients.

First, the individual prognostic ability of the antibodies of Appendix Cwas refined using samples from the HH breast cohort that was describedin Example 2. In particular, certain antibodies were excluded based onsubjective assessment of specificity and scoreability. The methodologyparalleled that used in Example 10 and the updated antibody data ispresented in Appendix E.

Second, prognostic panels in two currently identified clinicallyimportant subclasses of breast cancer patients were generated, namelyER+/node− patients and ER− patients. To minimize the chance ofidentifying spurious associations, only those antibodies from Appendix Ethat showed sufficient significance (p-value of less than 0.10) ineither the ER+ or ER+/node− patient classes were used in creatingprognostic panels for the ER+/node− patients, and only the similarlysignificant markers from the ER− patient set for creating a prognosticpanel for the ER− patients. Using Cox proportional hazard analysis andregression tree analysis (as described in Example 10) candidate panels(and dendrograms for regression tree analysis) were derived forprediction of early recurrence. For both ER+/node− patients and ER−patients, panels and dendrograms were chosen that identified patientswith significantly increased risks of recurrence.

Prognostic Panels Generated by Cox Analysis

Cox proportional hazard analysis treats the component antibodies of apanel as additive risk factors. The panels for the specified patientclasses were created by initially using all applicable antibodies, andthen iteratively removing antibodies from the panel. If the removal ofan antibody increased or did not affect the significance and prognosticability of the panel as a whole, it was excluded, otherwise it wasretained. In this manner preferred panels with minimal numbers ofantibodies were created. The preferred panels for ER+/node− and ER−patients are presented in Tables 5 and 6, respectively. Antibodieswithin the preferred panels are ranked based on their relativecontributions to the overall prediction function.

TABLE 5 Panel Analysis P value¹ Hazard ratio² Breast ER+/node- Cox8.17E−05 5.68 AGI ID Rank P value³ Terms⁴ S0702/s0296P1 1 0.00015−0.213, 1.330 s6006 2 0.00660 −0.325, 0.799 s0404 3 0.06200 −0.099,0.958 s0545 4 0.10000 −0.112, 0.604 s0235 5 0.25000 −0.114, 0.390 ¹Pvalue of overall panel ²Hazard ratio of overall panel ³P value of thecontribution of a given antibody to the overall panel ⁴Contribution ofgiven antibody to overall panel prediction function depending on IHCscore (e.g., scores of 0 or 1 for s6006 which uses scoring method 2 [seeAppendix E] result in its term in the model equaling −0.325 or 0.799,respectively).

TABLE 6 Panel Analysis P value¹ Hazard ratio² Breast ER− Cox 3.10E−032.25 AGI ID Rank P value³ Terms⁴ s0691 1 0.04700 −0.163, 0.436, 0.640s0545 2 0.08900 −0.339, 0.259 s0330x1 3 0.57000 0.510, −5.560^(1,2,3,4)See Table 5

The prognostic value of these exemplary panels were assessed bygenerating Kaplan-Meier recurrence curves for ER+/node− and ER−patients. Patients whom the panels predicted as being strongly likely torecur were placed in the “bad” prognosis group. Patients whom the panelspredicted as being strongly unlikely to recur were given the predictionof “good”. Patients whom the panels predicted as neither being stronglylikely to recur or not recur were placed in the “moderate” prognosisgroup. Kaplan-Meier curves were then calculated based on recurrence datafor patients within each group. FIG. 6 shows the curves that wereobtained for ER+/node− patients in each of these prognostic groups. FIG.7 shows the curves that were obtained for ER− patients in each of theseprognostic groups.

When lymph node status was included as an additional variable for theER− patient set the preferred panel was as shown in Table 7.

TABLE 7 Panel Type P value¹ Hazard ratio² Breast ER− Cox plus node3.70E−05 3.93 AGI ID Rank P value³ Terms⁴ s6007 1 0.05000 −0.460, 0.280s0545 2 0.06400 −0.400, 0.290 s0068 3 0.18000 −0.350, 0.160 s0330x1 40.62000 −5.820, 0.450 ^(1,2,3,4)See Table 5

The prognostic value of this exemplary panel was also assessed bygenerating Kaplan-Meier recurrence curves for ER− patients. Patientswhom the panel predicted as being strongly likely to recur were placedin the “bad” prognosis group. Patients whom the model predicted as beingstrongly unlikely to recur were given the prediction of “good”. Patientswhom the model predicted as neither being strongly likely to recur ornot recur were placed in the “moderate” prognosis group. Kaplan-Meiercurves were then calculated based on recurrence data for patients withineach group. FIG. 8 shows the curves that were obtained for ER− patientsin each of these prognostic groups.

While the preferred Cox panels of the invention for ER+/node− and ER−patients include each of the listed antibodies, it is to be understoodthat other related panels are encompassed by the present invention. Inparticular, it will be appreciated that the present invention is in noway limited to the specific antibodies listed. For example, otherantibodies directed to the same biomarker(s) may be used (e.g., takingthe Cox ER+/node− panel, it can be readily seen from Appendix A thatantibodies s0702 or s0296P1 can be replaced with other antibodiesdirected to biomarker Hs.184601; antibody s6006 can be replaced withother antibodies directed to biomarker Hs.1846, etc.). As noted,addition of certain antibodies from Appendix E had no effect on thesignificance and prognostic ability of the panel as a whole. Thus,antibodies may be added to any given panel without necessarilydiminishing the utility of a panel for patient prognosis. The inclusionof antibodies beyond those listed in Appendix E is also within the scopeof the invention. In certain embodiments less than all of the listedantibodies may be used in a prognostic panel.

Generally, a Cox panel for ER+/node− patients will include at least twoantibodies selected from the group consisting of antibodies directed tobiomarkers Hs.184601, Hs.1846, Hs.75789, Hs.63609 and Hs.220529 (e.g.,s0702 and/or s0296P1, s6006, s0404, s0545 and s0235, see Table 5 andAppendix A). Preferably, the panel will include an antibody directed tobiomarker Hs.184601 and at least one antibody directed to a biomarkerselected from the group consisting of Hs.1846, Hs.75789, Hs.63609 andHs.220529. All permutations of these antibodies are encompassed. In oneembodiment an antibody to biomarker Hs.184601 (e.g., s0702 and/ors0296P1) is used with an antibody to biomarker Hs.1846 (e.g., s6006). Inanother embodiment an antibody to biomarker Hs.184601 is used withantibodies to biomarkers Hs.1846 and Hs.75789 (e.g., s6006 and s0404).In other embodiments an antibody to biomarker Hs.184601 is used withantibodies to biomarkers Hs.1846, Hs.75789, Hs.63609 and optionallyHs.220529 (e.g., s6006, s0404, s0545 and optionally s0235). In preferredembodiments an antibody to Hs.184601 is used with antibodies tobiomarkers Hs.1846, Hs.75789, Hs.63609 and Hs.220529.

Similarly, a Cox panel for ER− patients will include at least twoantibodies selected from the group consisting of antibodies directed tobiomarkers Hs.6682, Hs.63609 and Hs.306098 (e.g., s0691, s0545 ands0330×1, see Table 6 and Appendix A). Preferably, the panel will includean antibody directed to biomarker Hs.6682 and antibodies to one or bothof biomarkers Hs.63609 and Hs.306098. In preferred embodiments anantibody to biomarker Hs.6682 is used with antibodies to biomarkersHs.63609 and Hs.306098.

When lymph node status is used as an additional variable, an inventiveprognostic Cox panel for ER− patients will include at least twoantibodies selected from the group consisting of antibodies directed tobiomarkers Hs.80976, Hs.63609, Hs.416854 and Hs.306098 (e.g., s6007,s0545, s0068 and s0330×1, see Table 7 and Appendix A). Preferably, thepanel will include an antibody directed to biomarker Hs.80976 andantibodies to one or more of biomarkers Hs.63609, Hs.416854 andHs.306098. All permutations of these antibodies are encompassed. In oneembodiment an antibody to biomarker Hs.80976 is used with an antibody tobiomarker Hs.63609. In another embodiment an antibody to biomarkerHs.80976 is used with antibodies to biomarkers Hs.63609 and Hs.416854and optionally with a biomarker to Hs.306098. In preferred embodimentsan antibody to biomarker Hs.80976 is used with antibodies to biomarkersHs.63609, Hs.416854 and Hs.306098.

The present invention also encompasses methods of assessing theprognosis of a patient having a breast tumor using these exemplarypanels. After obtaining a tumor sample from a patient with unknownprognosis the sample is contacted with two or more antibodies from thepanels of Tables 5, 6 and/or 7. The patient's likely prognosis is thenassessed based upon the pattern of positive and negative binding of thetwo or more antibodies to the tumor sample.

Prognostic Panels Generated by Regression Tree Analysis

Regression trees classify the patients into a number of subclasses eachdefined by their pattern of binding to a unique set of antibodies fromwithin a panel. An exemplary tree (or “dendrogram”) for ER+/node−patients is shown in FIG. 9 which is discussed below. Regression treeswere initially created with all applicable antibodies, and then “pruned”to a minimal complexity (least number of terminal nodes without losingtoo much prognostic ability) using a cross validation procedure. Thiscross validation procedure involved building panels and dendrogramsusing a series of patient groups that were picked from the total patientset using a series of increasingly pruned trees. The results over thetested groups were summed and the minimally complex least error-pronepanel and dendrogram were chosen. The resulting dendrogram was furthersimplified by placing nodes with a range of response values into theclasses “good” or “poor” (or alternatively “good”, “moderate” or“poor”). Table 8 lists the antibodies of an exemplary ER+/node− treepanel that was constructed as described above. The dendrograms for thispanel is illustrated in FIG. 9.

TABLE 8 Panel Analysis P value¹ Hazard ratio² Breast ER+/node- Tree2.82E−05 6.06 AGI ID Rank s0702/s0296P1 1 s0081 2 s6006 2 s6007 3 s05454 s6002 4 ¹P value of overall panel ²Hazard ratio of overall panel

As illustrated in FIG. 9, if a patient is positive for staining at agiven node his or her prognosis decision tree follows the branch markedwith a “+”. Conversely if a patient is negative for staining at a givennode his or her prognosis decision tree follows the branch marked “−”.This is done until a terminus is reached.

For example, if patient A is positive for staining with s0702 andnegative for staining with s0081 then, based on the dendrogram, his orher prognosis is “bad”. In contrast, if patient B is negative forstaining with s0702, negative for staining with s6006, positive forstaining with s6007 and negative for staining with s0545 then his or herprognosis is “good”. It will be appreciated from the foregoing and FIG.9 that the number of stains required in order to yield a prognosis willvary from patient to patient. However, from a practical standpoint (andwithout limitation), it may prove advantageous to complete all thestains for a given panel in one sitting rather than adopting aniterative approach with each individual antibody.

The prognostic value of the exemplary panel of Table 8 was also assessedby generating Kaplan-Meier recurrence curves for ER+/node− patients.Patients whom the panel predicted as being strongly likely to recur wereplaced in the “bad” prognosis group. Patients whom the panel predictedas being strongly unlikely to recur were given the prediction of “good”.Patients whom the panel predicted as neither being strongly likely torecur or not recur were placed in the “moderate” prognosis group.Kaplan-Meier curves were then calculated based on recurrence data forpatients within each group. FIG. 10 shows the curves that were obtainedfor ER+/node− patients in each of these prognostic groups.

Generally, a tree panel for ER+/node− patients will include an antibodyto biomarker Hs.184601 (e.g., s0702 or s0296P1) with antibodies to oneor both of biomarkers Hs.155956 and Hs.1846 (e.g., s0081 and s6006, seeTable 8 and Appendix A). In certain embodiments an antibody to biomarkerHs.80976 (e.g., s6007) may be added, optionally with antibodies to oneor both of biomarkers Hs.63609 and Hs.2905 (e.g., s0545 and s6002). Inpreferred embodiments, the tree panel includes an antibody to biomarkerHs.184601 and antibodies to biomarkers Hs.155956, Hs.1846, Hs.80976,Hs.63609 and Hs.2905.

Table 9 lists the antibodies of exemplary ER+ and ER− tree panels thatwere constructed as described above for the ER+/node− tree panel ofTable 8. The dendrograms for theses panels are illustrated in FIG. 11.

TABLE 9 Panel Analysis Panel Analysis Breast ER+ Tree Breast ER− TreeAGI ID Rank AGI ID Rank s0702/s0296P1 1 s6007 1 s0137 2 s0303 2 s6007 2s0398 2 s5076 3 s0063 3 s0143 3 s0545 4 s6007 4 s0702/s0296P1 4 s0545 4s0068 5

The present invention also encompasses methods of assessing theprognosis of a patient having a breast tumor using an inventive treepanel. For example, after obtaining a tumor sample from a patient withunknown prognosis the sample is contacted with two or more antibodiesfrom the panel of Table 8 (or one of the panels in Table 9). Thepatient's likely prognosis is then assessed based upon the positive ornegative binding of the two or more antibodies to the tumor sample usingthe dendrogram of FIG. 9 (or FIG. 11). Taking the ER+/node− panel ofTable 8 as an example, the method generally includes a step ofcontacting the tumor sample with an antibody to biomarker Hs.184601(e.g., s0702 or s0296P1) and antibodies to one or both of biomarkersHs.155956 and Hs.1846 (e.g., s0081 and/or s6006). In other embodimentsthe tumor sample is further contacted with an antibody to biomarkerHs.80976 (e.g., s6007) and optionally with antibodies to biomarkersHs.63609 and/or Hs.2905 (e.g., s0545 and/or s6002). As mentioned above,the tumor sample may be contacted with these antibodies in a singlesitting or sequentially based on the binding results of a previousstain. Obviously the tumor sample may be divided and differentantibodies contacted with different fractions. Alternatively differentoriginal tumor samples may be contacted with different antibodies.

Whether created by Cox or regression tree analysis, the exemplaryprognostic panels were determined to be independent of age, stage, andgrade. To ensure that the panels were not identifying classes ofpatients unlikely to be found to be significant in an independentcohort, cross validation was used to estimate the error inherent in eachpanel. Ten-fold cross-validation was performed by sequentially“leaving-out” 10% of patients and building panels on the remainingpatients ten times such that all patients were ultimately classified.This included re-determining the set of antibodies sufficientlysignificant to be employed in the panel building process (p-value<0.10). Cross validated p-values reflect the confidence calculated forthe sum of the ten independent panels and confirmed the statisticalsignificance of these panels. For the ER+/node− patient set, both theCox (Table 5) and regression tree (Table 8) panels showed significanceafter cross-validation (p-value/hazard ratio of 1.12E-02/2.36 and3.40E-03/2.91, respectively). For the ER− patient set, the Cox panels(Tables 6-7) were also shown to be able to retain significance(p-value/hazard ratios of 6.40E-02/1.37 and 1.80E-03/1.79 for the panelsof Table 6 and 7, respectively).

It is to be understood that each of the exemplary Cox and tree panelsdescribed herein may be used alone, in combination with one another(e.g., the Cox panel of Table 5 and the tree panel of Table 8 forER+/node− patients) or in conjunction with other panels and/orindependent prognostic factors.

Example 13 Prognostic Lung Cancer Panels

This Example builds on the results of Example 11 and describes theidentification of exemplary panels of antibodies whose binding has beenshown to correlate with the prognosis of lung cancer patients.

Prognostic panels in two currently identified clinically importantsubclasses of lung cancer patients were generated, namely adenocarcinomaand squamous cell carcinoma patients. Consistent with the known clinicalsignificance of diagnoses of these two subclasses of lung cancerpatients it was found that the most robust models were derived whenpatients were first classified in this manner, and then the separatepatient groups modeled independently. It will be appreciated that thisapproach is non-limiting and that models could be generated using alllung cancer patients or using other subclasses of patients. To minimizethe chance of identifying spurious associations, only those antibodiesfrom Appendix D that showed sufficient significance (p-value of lessthan 0.10) in the adenocarcinoma patient class were used in creatingprognostic panels for the adenocarcinoma patients, and only thesimilarly significant markers from the squamous cell carcinoma patientclass for creating a prognostic panel for the squamous cell carcinomapatients. Using Cox proportional hazard analysis (as described inExample 10) candidate panels were derived for prediction of earlyrecurrence. For both adenocarcinoma and squamous cell carcinomapatients, panels were chosen that identified patients with significantlyincreased risks of recurrence.

As previously noted, Cox proportional hazard analysis treats thecomponent antibodies of a panel as additive risk factors. The panels forthe specified patient classes were created by initially using allapplicable antibodies, and then iteratively removing antibodies from thepanel. If the removal of an antibody increased or did not affect thesignificance and prognostic ability of the panel as a whole, it wasexcluded, otherwise it was retained. In this manner preferred panelswith minimal numbers of antibodies were created. The preferred panelsfor squamous cell carcinoma and adenocarcinoma patients are presented inTables 10 and 11, respectively. Antibodies within the preferred panelsare ranked based on their relative contributions to the overallprediction function.

TABLE 10 Panel Analysis P value¹ Hazard ratio² Lung squamous Cox3.20E−05 6.88 AGI ID Rank P value³ Terms⁴ s0022 1 0.00620 0.880, −1.240s0702/s0296P1 2 0.12000 0.980, −0.150 s0330 3 0.13000 0.870, −0.034s0586 4 0.16000 0.680, −0.250 ¹P value of overall panel ²Hazard ratio ofoverall panel ³P value of the contribution of a given antibody to theoverall panel ⁴Contribution of given antibody to overall panelprediction function depending on IHC score (e.g., scores of 0 or 1 fors0022 which uses scoring method 2 [see Appendix D] result in its term inthe model equaling 0.880 or −1.240, respectively).

TABLE 11 Panel Analysis P value¹ Hazard ratio² Lung Cox 1.30E−05 3.23adenocarcinoma AGI ID Rank P value³ Terms⁴ s6013 1 0.02000 −0.430, 0.520s0545 2 0.03500 −0.070, 1.150 s0404 3 0.04000 −0.270, 0.550s0702/s0296P1 4 0.08800 −0.230, 0.450 ^(1,2,3,4)See Table 10

The prognostic value of these exemplary panels were assessed bygenerating Kaplan-Meier recurrence curves for the combined lung cancerpatients of the HH lung cohort. Patients were initially classified asadenocarcinoma or squamous cell carcinoma patients. For each patient thepattern of antibody staining with the applicable panel (i.e., Table 10or 11) was then assessed. Patients whom the panels predicted as beingstrongly likely to recur were placed in the “bad” prognosis group.Patients whom the panels predicted as being strongly unlikely to recurwere given the prediction of “good”. Patients whom the panels predictedas neither being strongly likely to recur or not recur were placed inthe “moderate” prognosis group. Kaplan-Meier curves were then calculatedbased on five year recurrence data for patients within each group. FIG.12 shows the curves that were obtained when the combined lung cancerpatients were placed in “good”, “moderate” or “bad” prognosis groups.FIG. 13 shows the curves that were obtained when patients in the“moderate” and “bad” groups were combined into a single “bad” prognosticgroup.

To ensure that the panels were not identifying classes of patientsunlikely to be found to be significant in an independent cohort, crossvalidation was used to estimate the error inherent in each panel.Ten-fold cross-validation was performed by sequentially “leaving-out”10% of patients and building panels on the remaining patients ten timessuch that all patients were ultimately classified. This includedre-determining the set of antibodies sufficiently significant to beemployed in the panel building process (p-value <0.10). Cross validatedp-values reflect the confidence calculated for the sum of the tenindependent panels and confirmed the statistical significance of thesepanels. The panels showed significance after cross-validation with thecombined lung patients (p-value/hazard ratio of 2.20E-02/1.48 when a“good”, “moderate” and “bad” scheme was used and 1.80E-02/2.07 when a“good” and “bad” scheme was used).

While preferred Cox panels of the invention for lung cancer patientsinclude each of the listed antibodies, it is to be understood that otherrelated panels are encompassed by the present invention. In particular,it will be appreciated that the present invention is in no way limitedto the specific antibodies listed. For example, other antibodiesdirected to the same biomarker(s) may be used (e.g., taking the squamouscell carcinoma panel, it can be readily seen from Appendix A thatantibody s0022 can be replaced with other antibodies directed tobiomarker Hs.176588; s0702 or s0296P1 can be replaced with otherantibodies directed to biomarker Hs.184601, etc.). Other antibodies fromAppendix D may be added to any given panel without necessarilydiminishing the utility of a panel for patient prognosis. The inclusionof antibodies beyond those listed in Appendix D is also within the scopeof the invention. In certain embodiments less than all of the listedantibodies may be used in a prognostic panel.

Generally, a Cox panel for squamous cell carcinoma patients will includeat least two antibodies selected from the group consisting of antibodiesdirected to biomarkers Hs.176588, Hs.184601, Hs.306098 and Hs.194720(e.g., s0022, s0702 or s0296P1, s0330 and s0586, see Table 10 andAppendix A). Preferably, the panel will include an antibody directed tobiomarker Hs.176588 and at least one antibody directed to a biomarkerselected from the group consisting of Hs.184601, Hs.306098 andHs.194720. All permutations of these antibodies are encompassed. In oneembodiment an antibody to biomarker Hs.176588 (e.g., s0022) is used withan antibody to biomarker Hs.184601 (e.g., s0702 and/or s0296P1). Inanother embodiment an antibody to biomarker Hs.176588 is used withantibodies to biomarkers Hs.184601 and Hs.306098 (e.g., s0702 or s0296P1and s0330). In preferred embodiments an antibody to biomarker Hs.176588is used with antibodies to biomarkers Hs.184601, Hs.306098 andHs.194720.

Similarly, a Cox panel for adenocarcinoma patients will include at leasttwo antibodies selected from the group consisting of antibodies directedto biomarkers Hs.323910, Hs.63609, Hs.75789 and Hs.184601 (e.g., s6013,s0545, s0404 and s0702 or s0296P1, see Table 11 and Appendix A).Preferably, the panel will include an antibody directed to biomarkerHs.323910 and at least one antibody directed to a biomarker selectedfrom the group consisting of Hs.63609, Hs.75789 and Hs.184601. Allpermutations of these antibodies are encompassed. In one embodiment anantibody to biomarker Hs.323910 (e.g., s6013) is used with an antibodyto biomarker Hs.63609 (e.g., s0545). In another embodiment an antibodyto biomarker Hs.323910 is used with antibodies to biomarkers Hs.63609and Hs.75789 (e.g., s0545 and s0404). In preferred embodiments anantibody to biomarker Hs.323910 is used with antibodies to biomarkersHs.63609, Hs.75789 and Hs.184601.

It is to be understood that these exemplary Cox panels may be usedalone, in combination with one another or in conjunction with otherpanels and/or independent prognostic factors.

The present invention also encompasses methods of assessing theprognosis of a patient having a lung tumor using these exemplary panels.After obtaining a tumor sample from a patient with unknown prognosis thesample is contacted with two or more antibodies from the panels of Table10 and/or 11. The patient's likely prognosis is then assessed based uponthe positive or negative binding of the two or more antibodies to thetumor sample.

Example 14 Use of Prognostic Lung Cancer Panels with an IndependentCohort

This Example builds on the results of Example 13 by describing how theexemplary prognostic lung cancer panels were used to predict recurrencein an independent cohort of lung cancer patients.

A cohort of 119 lung cancer patients from the University ofAlabama-Birmingham (UAB) was used for this purpose. Relatively limitedclinical data was available for these patients, in most cases onlysurvival time was available. The average time of follow-up amongpatients who did not die of disease was 28 months. Of the 119 patients,54 were noted to have had a recurrence of cancer within the studyperiod, and 74 died of disease. This cohort differed significantly fromthe HH lung cohort (see Example 11) in that it was not limited to earlystage tumors, and therefore the cohort had a greater incidence of deathdue to disease. Since recurrence data for this cohort was limited, theprognostic panels of Example 13 (designed to predict recurrence) wereused to predict survival in this independent cohort. Specifically, theprognostic value of the panels were assessed by generating Kaplan-Meiersurvival curves for the combined lung cancer patients of the UAB cohort.As in Example 13, patients were initially classified as adenocarcinomaor squamous cell carcinoma patients. For each patient the pattern ofantibody staining with the applicable panel (i.e., Table 10 or 11) wasthen assessed. Patients were placed in “bad”, “moderate” and “good”prognosis groups based on their binding patterns with these antibodies.Kaplan-Meier curves were then calculated based on survival data forpatients within each group. FIG. 14 shows the curves that were obtainedwhen the combined lung cancer patients were placed in “good”, “moderate”or “bad” prognosis groups (p-value/hazard ratio of 5.20E-02/1.98). FIG.15 shows the curves that were obtained when the patients in the“moderate” and “bad” groups were combined into a single “bad” prognosticgroup (p-value/hazard ratio of 2.50E-02/3.03). FIG. 16 shows the curvesthat were obtained when the subclass of adenocarcinoma patients wereplaced in “good”, “moderate” or “bad” prognosis groups (no patients fellwithin the “bad” group hence there are only two curves, p-value/hazardratio of 4.00E-02/2.19). FIG. 17 shows the curves that were obtainedwhen the subclass of squamous cell carcinoma patients were placed in“good”, “moderate” or “bad” prognosis groups (p-value/hazard ratio of2.50E-02/3.03).

The prognostic significance of individual antibodies identified in theHH lung cohort (i.e., those listed in Appendix D) were also reassessedusing the five year survival data from the UAB cohort. The methodologywas as described in Example 11. The prognostic significance of thesesame antibodies was also recalculated using five year recurrence datafrom the HH lung cohort (instead of the complete follow-up period as inExample 11 where patients who did not die of disease were followed for aperiod of up to ten years). Based on these calculations, severalantibodies from Appendix D were found to have a relatively significantindividual prognostic value (p-value less than 0.2) in both the HH andUAB lung cohorts. These antibodies are presented in Appendix F.

Example 15 Use of a Lung Cancer Classification Panel with an IndependentCohort

The pattern of reactivity with the lung cancer classification panel ofExample 5 (see Appendix A) was determined using samples from the HH lungcohort (data not shown). As in Example 4, patients were classified usingk-means clustering. Seven sub-classes of lung cancer patients werechosen by their consensus pattern of staining.

The morphology of the lung cancers within each sub-class were determinedand are shown graphically in FIG. 18. Interestingly, the sub-classeswere found to comprise patients with lung cancers having similarmorphological characteristics (i.e., sub-classes 1, 2, 3 and 7 werecomposed of a majority of patients with adenocarcinomas; sub-classes 4and 5 were composed of a majority of patients with squamous cellcarcinomas and sub-class 6 was composed of a majority of patients withlarge cell carcinomas). These results suggest that the antibodies in theclassification panel are recognizing biological and clinical diversityin lung cancers.

Out of interest, the prognostic value of these seven sub-classes wasalso assessed. (Note that these sub-classes were constructed based onsample staining patterns across the entire classification panel ofAppendix A. This differs from the approach that was described in Example14 where specific antibodies with predetermined prognostic value werecombined into prognostic panels that were then used to classifypatients). The average probability of recurrence for the overall HHcohort was first calculated and found to level out at about 38% aftersix years. Average probabilities within each of the seven HH sub-classeswere then calculated and compared with the overall average. Sub-classeswith an average probability of recurrence after six years that wasgreater than 48% (i.e., more than 10% worse than the overall population)were classified as having a “bad” prognosis. Sub-classes with an averageprobability of recurrence after six years that was less than 28% (i.e.,more than 10% better than the overall population) were classified ashaving a “good” prognosis. Sub-classes with an average probability ofrecurrence after six years of 28 to 48% were classified as having a“moderate” prognosis. Based on this analysis, HH sub-classes 1, 6 and 7were classified as “bad”; HH sub-classes 2 and 4 as “moderate”; and HHsub-classes 3 and 5 as “good”. When the recurrence data for patients inthe “bad”, “moderate” and “good” classes were combined and plotted asKaplan-Meier curves the different outcomes for the three prognosticgroups were statistically significant (p-value <0.02, data not shown).

In order to assess whether the sub-classes of FIG. 18 would correlateacross lung cancers in general, the k-means clustering criteria thatwere used in classifying the HH lung cohort were “forced” onto samplesfrom an independent lung cohort (namely the UAB lung cohort that wasdescribed in Example 14). Note that while the HH lung cohort wascomposed of Stage I/II patients, the UAB lung cohort was composed ofStage III/IV patients. Thus, overall the prognosis of UAB patients wasworse than the prognosis of HH patients. First, the mean values from theHH k-means analysis were calculated for each of the seven HH sub-classesof FIG. 18. Antibody staining results for each UAB sample were thencompared with all seven means and samples were assigned to one of theseven “HH sub-classes” based on the closest match. The seven UABclusters were then classified as having a “bad”, “moderate” and “good”prognosis based simply on the prognoses that had been previouslydetermined for the corresponding seven HH sub-classes (see above). Whenthe recurrence data for patients in the “bad”, “moderate” and “good”classes were combined and plotted as Kaplan-Meier curves the differentoutcomes for the three prognostic groups were again statisticallysignificant (p-value <0.02, data not shown). Examination of the curvesand subsequent analysis showed that “good” and “moderate” gave similaroutcomes relative to each other while “bad” was clearly divergent fromthese two.

Other Embodiments

Other embodiments of the invention will be apparent to those skilled inthe art from a consideration of the specification or practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with the true scope of theinvention being indicated by the following claims.

APPENDIX A BREAST LUNG COLON NCBI PANEL? PANEL? PANEL? LocusLink UniGeneAGI ID GENE NAME Russ. HH Russ. HH Russ. ID ID ALIASES S0011 VAV-3protein X 10451 Hs.267659 VAV3; VAV3 ONCOGENE; ONCOGENE VAV3; vav 3oncogene S0018 mammoglobin X 4250 Hs.46452 UGB2; MGB1; mammaglobin 1;MAMMAGLOBIN A PRECURSOR S0020 RB18A (P53 binding) X 5469 Hs.15589 RB18A;TRIP2; PPARGBP; PBP; CRSP1; PPARBP; CRSP200; DRIP230; PPAR-BINDINGPROTEIN; PPARG binding protein; PPAR binding protein; CRSP, 200- KDSUBUNIT; PEROXISOME PROLIFERATOR-ACTIVATED RECEPTOR-BINDING PROTEIN;THYROID HORMONE RECEPTOR INTERACTOR 2; RECOGN S0021 Putativecadherin-like X X X X 222256 Hs.12496 FLJ23834 protein S0022 Sim to Cytop450 X X X X X 199974 Hs.176588 cytochrome P450 4Z1 S0024 Chuck Ras X85004 Hs.21594 RERG; RAS-like, estrogen-regulated, growth-inhibitorS0032 MDGI X 2170 Hs.49881 MDGI; O-FABP; FABP3; FABP11; H- FABP; FATTYACID-BINDING PROTEIN, SKELETAL MUSCLE; Fatty acid-binding protein 3,muscle; fatty acid binding protein 11; FATTY ACID-BINDING PROTEIN,MUSCLE AND HEART; fatty acid binding protein 3, muscle and heart(mammary-de S0036 Human GABA-A receptor X 2568 Hs.70725 GABRP;GAMMA-AMINOBUTYRIC pi subunit ACID RECEPTOR, PI; GABA-A RECEPTOR, PIPOLYPEPTIDE; gamma-aminobutyric acid (GABA) A receptor, pi S0037 AnnexinVIII X 244 Hs.87268 ANX8; ANXA8; annexin VIII; annexin A8 S0039hypothetical UBCc X X X X Hs.351357 containing protein(Ubiquitin-conjugating enzyme E2, catalytic domain homologues) S0040MDR1 X X X X 5243 Hs.21330 GP170; PGY1; P-gp; ABCB1; ABC20; MDR1;DOXORUBICIN RESISTANCE; P-GLYCOPROTEIN 1; multidrug resistance 1; P-glycoprotein-1/multiple drug resistance-1; MULTIDRUG RESISTANCE PROTEIN1; ATP- BINDING CASSETTE, SUBFAMILY B, MEMBER 1; P glycoprotein1/multiple dr S0041 MDR3 X 5244 Hs.73812 MDR2; MDR3; PGY3; PFIC-3;ABCB4; ABC21; MDR2/3; P- GLYCOPROTEIN 3; MULTIDRUG RESISTANCE 3;P-glycoprotein- 3/multiple drug resistance-3; MULTIDRUG RESISTANCEPROTEIN 3; P glycoprotein 3/multiple drug resistance 3; ATP- bindingcassette, sub-family B (MDR/TAP S0042 MRP-1 X X X X 4363 Hs.89433 MRP;MRP1; GS-X; ABC29; ABCC1; ABCC; multidrug resistance protein; multipledrug resistance-associated protein; MULTIDRUG RESISTANCE- ASSOCIATEDPROTEIN 1; multiple drug resistance protein 1; ATP- BINDING CASSETTE,SUBFAMILY C, MEMBER 1; ATP-binding cassett S0043 MRP2/CMOAT X X X X 1244Hs.193852 MRP2; cMRP; CMOAT; ABCC2; ABC30; ABC#; CMOAT1; DJS; MULTIDRUGRESISTANCE- ASSOCIATED PROTEIN 2; CANALICULAR MULTIDRUG RESISTANCEPROTEIN; MULTISPECIFIC ORGANIC ANION TRANSPORTER, CANALICULAR;CANALICULAR MULTISPECIFIC ORGANIC ANION TRANSPORTER 1; ATP-BINDI S0044MRP4 X X X X 10257 Hs.139336 MOAT-B; MRP4; ABCC4; MOATB; EST170205;MRP/CMOAT- RELATED ABC TRANSPORTER; MULTIDRUG RESISTANCE- ASSOCIATEDPROTEIN 4; MULTISPECIFIC ORGANIC ANION TRANSPORTER B; ATP-bindingcassette, sub-family C, member 4; ATP-BINDING CASSETTE, SUBFAMILY C,MEMBER 4; ATP- S0045 MRP3/CMOAT2 X 8714 Hs.90786 MOAT-D; MRP3; ABCC3;MLP2; ABC31; EST90757; CMOAT2; MULTIDRUG RESISTANCE- ASSOCIATED PROTEIN3; canicular multispecific organic anion transporter; ATP-BINDINGCASSETTE, SUBFAMILY C, MEMBER 3; CANALICULAR MULTISPECIFIC ORGANIC ANIONTRANSPORTER 2; ATP-bindi S0046 MRP5/MOAT-C X X X X 10057 Hs.108660ABCC5; MRP5; EST277145; ABC33; SMRP; pABC11; MOATC; ABC TRANSPORTERMOAT-C; MULTIDRUG RESISTANCE- ASSOCIATED PROTEIN 5; canalicularmultispecific organic anion transporter C; ATP-binding cassette,sub-family C, member 5; ATP-BINDING CASSETTE, SUBFAMILY C, S0047 MRP6 XX X 368 Hs.274260 MOAT-E; MRP6; ARA; EST349056; ABCC6; MOATE; PXE; MLP1;ABC34; pseudoxanthoma elasticum; ANTHRACYCLINE RESISTANCE- ASSOCIATEDPROTEIN; MULTIDRUG RESISTANCE- ASSOCIATED PROTEIN 6; ATP- BINDINGCASSETTE, SUBFAMILY C, MEMBER 6; ATP-binding cassette, sub-family CS0048 BSEP X 8647 Hs.158316 BSEP; ABCB11; PFIC-2; SPGP; PGY4; PFIC2;ABC16; SISTER OF P-GLYCOPROTEIN; bile salt export pump; progressivefamilial intrahepatic cholestasis 2; ABC member 16, MDR/TAP subfamily;ATP-BINDING CASSETTE, SUBFAMILY B, MEMBER 11; ATP- binding cassette,sub-fam S0049 ATP-binding cassette, X X 23456 Hs.1710 MTABC2; EST20237;MABC2; M- sub-family B, member 10 ABC2; ABCB10; MITOCHONDRIAL ABCPROTEIN 2; ATP-BINDING CASSETTE, SUBFAMILY B, MEMBER 10; ATP-bindingcassette, sub-family B, member 10; ATP- binding cassette, sub-family B(MDR/TAP), member 10 S0050 TAP1 X X 6890 Hs.352018 S0052 SUR1 X X X 6833Hs.54470 SUR1; MRP8; ABC36; SUR; HI; ABCC8; HRINS; PHHI; sulfonylureareceptor (hyperinsulinemia); SULFONYLUREA RECEPTOR 1; SULFONYLUREARECEPTOR, BETA-CELL HIGH-AFFINITY; ATP- binding cassette, sub-family C,member 8; ATP-BINDING CASSETTE, SUBFAMILY C, MEMBER 8; A S0053 SUR2 X X10060 Hs.248960 SUR2; ABC37; ABCC9; sulfonylurea receptor 2A;ATP-BINDING CASSETTE, SUBFAMILY C, MEMBER 9; ATP-binding cassette,sub-family C (CFTR/MRP), member 9; ATP-binding cassette, sub-family C,member 9, isoform SUR2A-delta- 14; ATP-binding cassette, sub-family C, mS0055 INTEGRAL MEMBRANE X 9445 Hs.239625 E25B; ABRI; E3-16; FBD; BRI2;PROTEIN 2B BRICD2B; ITM2B; BRI GENE; (TRANSMEMBRANE BRICHOS domaincontaining 2B; PROTEIN BRI) integral membrane protein 2B S0057 ANK-3 X288 Hs.75893 ankyrin-G; ANK3; ankyrin-3, node of Ranvier; ankyrin 3isoform 1; ankyrin 3 isoform 2; ankyrin 3, node of Ranvier (ankyrin G)S0058 hypothetical protein X 80004 Hs.282093 FLJ21918; hypotheticalprotein FLJ21918 (RNA FLJ21918 recognition motif (RRM) containingprotein) S0059 ataxia-telangiectasia X X X X 23650 Hs.82237 ATDC;TRIM29; tripartite motif- group D-associated containing 29;ataxia-telangiectasia protein group D-associated protein; tripartitemotif protein TRIM29 isoform alpha; tripartite motif protein TRIM29isoform beta S0059P2 ataxia-telangiectasia X X 23650 Hs.82237 ATDC;TRIM29; tripartite motif- group D-associated containing 29;ataxia-telangiectasia protein group D-associated protein; tripartitemotif protein TRIM29 isoform alpha; tripartite motif protein TRIM29isoform beta S0063 iroquois related X X X X 79191 Hs.3321 homeobox 3S0068 Chuck Ras C-term X 85004 Hs.416854 RERG; hypothetical proteinMGC15754; RAS-like, estrogen- regulated, growth-inhibitor S0070 putativeG protein- X 26996 Hs.97101 GPCR150; GPR160; putative G coupled receptorprotein-coupled receptor; G protein- (NP_055188.1|) coupled receptor 160S0072 Calgranulin A X X 6279 Hs.100000 MRP-8; MIF; S100A8; MRP8; CFAG;L1Ag; 60B8AG; P8; CGLA; CAGA; NIF; MA387; CP-10; CYSTIC FIBROSISANTIGEN; MIGRATION INHIBITORY FACTOR-RELATED PROTEIN 8; LEUKOCYTE L1COMPLEX LIGHT CHAIN; S100 calcium-binding protein A8 (calgranulin A)S0073 hepatocyte nuclear X 3169 Hs.70604 HNF3A; MGC33105; TCF3A; protein3, alpha FOXA1; forkhead box A1; HEPATOCYTE NUCLEAR FACTOR 3-ALPHA;hepatocyte nuclear factor 3, alpha S0073P2 hepatocyte nuclear X X 3169Hs.70604 HNF3A; MGC33105; TCF3A; protein 3, alpha FOXA1; forkhead boxA1; HEPATOCYTE NUCLEAR FACTOR 3-ALPHA; hepatocyte nuclear factor 3,alpha S0074 intestinal trefoil precursor X X X X 7033 Hs.82961 TFF3;trefoil factor 3 (intestinal); trefoil factor 3, HITF, human intestinaltrefoil factor S0074P3 intestinal trefoil precursor X 7033 Hs.82961TFF3; trefoil factor 3 (intestinal); trefoil factor 3, HITF, humanintestinal trefoil factor S0076x1 Keratin 17 X 3872 Hs.2785 PCHC1; PC;PC2; 39.1; KRT17; K17; CYTOKERATIN 17; VERSION 1; CK 17; KERATIN, TYPE ICYTOSKELETAL 17 S0079 LIV-1 X X 25800 Hs.79136 LIV-1; LIV-1 protein,estrogen regulated S0081 NAT1 X X 9 Hs.155956 MNAT; NAT-1; AAC1; NAT1;ACETYL-CoA:ARYLAMINE N- ACETYLTRANSFERASE; EC 2.3.1.5; arylamine N-acetyltransferase-1; N- ACETYLTRANSFERASE TYPE 1; ARYLAMINE N-ACETYLTRANSFERASE, MONOMORPHIC; ARYLAMINE N- ACETYLTRANSFERASE 1; N-acetyltransferase 1 (arylamine N-ace S0086 x-box binding X 7494Hs.149923 XBP2; TREB5; XBP1; X-box-binding protein-1; X BOX-BINDINGPROTEIN 1; X BOX-BINDING PROTEIN 2; X-box binding protein 1 S0096ATPase, H+ transporting, X X 525 Hs.64173 ATP6B1; VATB; 3.6.1.34; VPP3;lysosomal (vacuolar RTA1B; EC 3.6.3.14; V-ATPASE B1 proton pump), betaSUBUNIT; VACUOLAR PROTON polypeptide, 56/58 kD, PUMP B ISOFORM 1;isoform 1R7340 ENDOMEMBRANE PROTON PUMP 58 KDA SUBUNIT; VACUOLAR ATPSYNTHASE SUBUNIT B, KIDNEY ISOFORM; ATPase, H+ TRANSPORTING, LYSOSOMAL,BETA SUBUNIT, ISOFORM 1; S0110 hypothetical protein X X X 84259 Hs.74284hypothetical protein MGC2714 MGC2714 (in part) S0117 Reproduction 8 X7993 Hs.153678 D8S2298E; REP8; reproduction 8; Reproduction/chromosome 8S0119 slit1 X 6585 Hs.133466 SLIT3; MEGF4; SLIL1; Slit-1; SLIT1; slithomolog 1 (Drosophila); SLIT, DROSOPHILA, HOMOLOG OF, 1; MULTIPLEEPIDERMAL GROWTH FACTOR-LIKE DOMAINS 4 S0132 sry-box9 X X X 6662 Hs.2316SRA1; CMD1; SOX9; CMPD1; CMPD1 SRY-BOX 9; transcription factor SOX9;TRANSCRIPTION FACTOR SOX-9; ACAMPOMELIC CAMPOMELIC DYSPLASIA; SRY-RELATED HMG-BOX GENE 9; SEX REVERSAL, AUTOSOMAL, 1; SRY (sex-determiningregion Y)-box 9 protein; SRY (sex-determining r S0137 EGF-Like Domain, XX X X X 1952 Hs.57652 EGFL2; CDHF10; KIAA0279; Multiple 2 CELSR2; MEGF3;EGF-like-domain, multiple 2; epidermal growth factor- like 2; multipleepidermal growth factor-like domains 3; cadherin EGF LAG seven-passG-type receptor 2; similar to cadherin-related tumor suppressor hFatprotein; S0139 Gamma-glytamyl X X 8836 Hs.78619 GGH; GH; EC 3.4.19.9;GAMMA- hydrolase GLU-X CARBOXYPEPTIDASE; GAMMA-GLUTAMYL HYDROLASEPRECURSOR; gamma-glutamyl hydrolase (conjugase, folylpolygammaglutamylhydrolase) precursor S0140 Bullous Pemphigoid X X X X 667 Hs.198689BP240; FLJ13425; FLJ32235; Antigen 1 FLJ21489; FLJ30627; CATX-15;KIAA0728; BPAG1; dystonin; hemidesmosomal plaque protein; bullouspemphigoid antigen 1, 230/240 kDa; bullous pemphigoid antigen 1 (230/240kD); bullous pemphigoid antigen 1 isoform 1eA precursor; bullo S0140P1Bullous Pemphigoid X 667 Hs.198689 BP240; FLJ13425; FLJ32235; Antigen 1FLJ21489; FLJ30627; CATX-15; KIAA0728; BPAG1; dystonin; hemidesmosomalplaque protein; bullous pemphigoid antigen 1, 230/240 kDa; bullouspemphigoid antigen 1 (230/240 kD); bullous pemphigoid antigen 1 isoform1eA precursor; bullo S0143 fasn X X X 2194 Hs.83190 FASN; FAS; EC2.3.1.85; EC 4.2.1.61; EC 1.3.1.10; EC 1.1.1.100; EC 3.1.2.14; EC2.3.1.41; EC 2.3.1.38; EC 2.3.1.39; fatty acid synthase S0143P3 fasn X X2194 Hs.83190 FASN; FAS; EC 2.3.1.85; EC 4.2.1.61; EC 1.3.1.10; EC1.1.1.100; EC 3.1.2.14; EC 2.3.1.41; EC 2.3.1.38; EC 2.3.1.39; fattyacid synthase S0144 Matrix Metalloproteinase X X 4323 Hs.2399 MMP-14;MMP-X1; MT1MMP; 14 MMP14; MTMMP1; MT1-MMP; EC 3.4.24.—; MT-MMP 1;membrane- type-1 matrix metalloproteinase; MATRIX METALLOPROTEINASE-14PRECURSOR; matrix metalloproteinase 14 (membrane- inserted);membrane-type matrix metalloproteinase 1; MATRIX METAL S0149 ABP/ZF X55503 Hs.302740 TRPV6; ECAC2; CAT1; CATL; CALCIUM TRANSPORTER 1; CALCIUMTRANSPORTER-LIKE PROTEIN; EPITHELIAL CALCIUM CHANNEL 2; transientreceptor potential cation channel, subfamily V, member 6 S0156 fattyacid binding protein X X X 2173 Hs.26770 B-FABP; FABP7; FABPB; MRG; 7,FABP7 mammary-derived growth inhibitor- related; FATTY ACID-BINDINGPROTEIN 7; FATTY ACID-BINDING PROTEIN, BRAIN; fatty acid binding protein7, brain S0158 Cadherin 3 X X X X 1001 Hs.2877 CDHP; HJMD; PLACENTAL-CADHERIN; PCAD; CDH3; placental cadherin; CADHERIN-3 PRECURSOR;CADHERIN, PLACENTAL; calcium-dependent adhesion protein, placental;cadherin 3, type 1 preproprotein; cadherin 3, type 1, P-cadherin(placental); cadherin 3, P-cadherin S0165 GRO1 Oncogene X X 2919 Hs.789GRO1; CXCL1; MGSA; SCYB1; GROA; GRO PROTEIN, ALPHA; GRO1 oncogene(melanoma growth- stimulating activity); MELANOMA GROWTH STIMULATORYACTIVITY, ALPHA; GRO1 oncogene (melanoma growth stimulating activity,alpha); SMALL INDUCIBLE CYTOKINE SUBFAMILY B, MEMBE S0171 Effector cellprotease X N/A Hs.1578 BIRC5; baculoviral IAP repeat- receptor 1containing 5 (survivin) S0193 PLOD2 X 5352 Hs.41270 PLOD2; LYSYLHYDROXYLASE 2; LYSINE HYDROXYLASE 2; PROCOLLAGEN-LYSINE, 2- OXOGLUTARATE5- DIOXYGENASE 2; procollagen- lysine, 2-oxoglutarate 5-dioxygenase(lysine hydroxylase) 2; procollagen- lysine, 2-oxoglutarate5-dioxygenase (lysine hydroxylase) 2 isoform S0211 Cytochrome p450, X X1549 Hs.250615 CYPIIA7; P450-IIA4; CPAD; CPA7; subfamily IIa CYP2A7; EC1.14.14.1; CYTOCHROME P450 2A7; cytochrome P450, subfamily IIA(phenobarbital-inducible), polypeptide 7, isoform 2; cytochrome P450,subfamily IIA (phenobarbital- inducible), polypeptide 7, isoform 1 S0218Putative nucleoside X 222962 Hs.4302 ENT4 transporter-like protein S0221Solute Carrier Family 28 X X 9153 Hs.193665 HCNT2; SLC28A2; CNT2; SPNT1;(sodium-coupled SPNT; CNT 2; nucleoside transporter), SODIUM/NUCLEOSIDEmember 2″ COTRANSPORTER 2; NA(+)/NUCLEOSIDE COTRANSPORTER 2;SODIUM/PURINE NUCLEOSIDE CO-TRANSPORTER; SODIUM- COUPLED NUCLEOSIDETRANSPORTER 2; CONCENTRATIVE NUCLEOSIDE TRANSPORTER 2; SODIUM- DEPENDENTPURIN S0223 Hepatic angiopoietin- X X X 51129 Hs.9613 HFARP; FIAF;ANGPTL4; PGAR; related protein angiopoietin-like 4; FASTING- INDUCEDADIPOSE FACTOR; PPARG ANGIOPOIETIN-RELATED PROTEIN; HEPATICFIBRINOGEN/ANGIOPOIETIN- RELATED PROTEIN; PPAR(gamma) angiopoietinrelated protein; PPAR(gamma) angiopoietin related gene; angiopoi S0235Carcinoembryonic X 1048 Hs.220529 CEA; CEACAM5; CD66e; antigen-relatedcell carcinoembryonic antigen-related cell adhesion molecule 5 adhesionmolecule 5 S0237 podocalyxin-like protein 1 X 5420 Hs.16426 PCLP; PODXL;PODOCALYXIN- LIKE PROTEIN S0241 glycyl-tRNA synthetase X X X 2617Hs.293885 S0251 LBP protein 32 X 29841 Hs.321264 LBP-32; LBP protein 32S0253 Putative Integral X X 55353 Hs.296398 LAPTM4beta; LC27; putativeintegral Membrane Transporter membrane transporter; lysosomal- (LC27)associated transmembrane protein 4 beta S0255 Cyclin E2 X X X X 9134Hs.408658 CYCE2; CCNE2; G1/S-SPECIFIC CYCLIN E2 S0260 KIAA0253 X X X23385 Hs.4788 KIAA0253; nicastrin; NCSTN S0265 FXYD domain-containing XX 5349 Hs.301350 MAT-8; MAT8; PLML; FXYD3; ion transport regulator 3phospholemman-like protein; MAMMARY TUMOR, 8-KD; FXYD domain-containingion transport regulator 3; FXYD domain containing ion transportregulator 3; FXYD domain containing ion transport regulator 3 isoform 2precursor; FXYD domai S0267 Immunoglobulin X 3321 Hs.81234 V8; IGSF3;immunoglobin Superfamily, Member 3 superfamily, member 3; immunoglobulinsuperfamily, member 3 S0270 STAM2 X 10254 Hs.17200 STAM2; DKFZp564C047;Hbp; hypothetical protein; SIGNAL- TRANSDUCING ADAPTOR MOLECULE 2;STAM-like protein containing SH3 and ITAM domains 2; signal transducingadaptor molecule (SH3 domain and ITAM motif) 2 S0273 Dickkopf Homolog 1X 22943 Hs.40499 DKK1; DKK-1; SK; dickkopf-1 like; dickkopf (Xenopuslaevis) homolog 1; dickkopf homolog 1 (Xenopus laevis); DICKKOPF,XENOPUS, HOMOLOG OF, 1 S0280 Putative Anion X 65010 Hs.298476 SLC26A6;DKFZP586E1422; solute Transporter 1 carrier family 26, member 6 S0286Wnt Inhibitory Factor 1 X 11197 Hs.284122 WIF1; WIF-1; Wnt inhibitoryfactor-1; WNT INHIBITORY FACTOR 1 S0288 PRAME X 23532 Hs.30743 MAPE;PRAME; OPA- INTERACTING PROTEIN 4; Opa- interacting protein OIP4;preferentially expressed antigen in melanoma; melanoma antigenpreferentially expressed in tumors S0295 Prostaglandin E X 9536Hs.146688 PGES; TP53I12; MGST1L1; PP1294; Synthase PP102; PTGES;MGC10317; PIG12; MGST1-L1; MGST-IV; MGST1-like 1; p53-INDUCED GENE 12;prostaglandin E synthase; p53- induced apoptosis protein 12;prostaglandin E synthase isoform 2; prostaglandin E synthase isoform 1;micros S0296 Solute Carrier Family 7, X X X X 8140 Hs.184601 SLC7A5;MPE16; D16S469E; E16; member 5/LAT1 protein 4F2LC; CD98; HLAT1; LAT1;CD98LC; 4F2 LC; 4F2 light chain; CD98 LIGHT CHAIN; INTEGRAL MEMBRANEPROTEIN E16; L-TYPE AMINO ACID TRANSPORTER 1; Solute carrier family 7,member 5; LARGE NEUTRAL AMINO ACIDS TRANSPORTER SMALL SUBUN S0296P1Solute Carrier Family 7, X X 8140 Hs.184601 SLC7A5; MPE16; D16S469E;E16; member 5/LAT1 protein 4F2LC; CD98; HLAT1; LAT1; CD98LC; 4F2 LC; 4F2light chain; CD98 LIGHT CHAIN; INTEGRAL MEMBRANE PROTEIN E16; L-TYPEAMINO ACID TRANSPORTER 1; Solute carrier family 7, member 5; LARGENEUTRAL AMINO ACIDS TRANSPORTER SMALL SUBUN S0297 v-maf X 7975 Hs.131953FLJ32205; NFE2U; MAFK; NFE2, musculoaponeurotic 18-KD SUBUNIT; nuclearfactor fibrosarcoma (avian) erythroid-2, ubiquitous (p18); oncogenefamily, protein NUCLEAR FACTOR ERYTHROID 2, K or NF-E2p18″ UBIQUITOUSSUBUNIT; v-maf musculoaponeurotic fibrosarcoma oncogene homolog K(avian); v-maf avian musculoaponeurotic fibrosarcoma oncogen S0301 CEGP1X 57758 Hs.222399 SCUBE2; signal peptide, CUB domain, EGF-like 2 S0303GABRE gamma- X X X X 2564 Hs.22785 GABRE; GABA(A) RECEPTOR; aminobutyricacid GABA-A RECEPTOR, EPSILON (GABA) A receptor, POLYPEPTIDE; GAMMA-epsilon AMINOBUTYRIC ACID RECEPTOR, EPSILON; GAMMA- AMINOBUTYRIC-ACIDRECEPTOR EPSILON SUBUNIT PRECURSOR; gamma-aminobutyric acid (GABA) Areceptor, epsilon, isoform 3; gamma- aminobutyric acid (G S0305 S100calcium-binding X X X 6281 Hs.400250 ANX2L; p10; GP11; S100A10; 42C;protein A10 ANX2LG; CAL1L; P11; CLP11; Ca[1]; P10 PROTEIN; CALPACTIN ILIGHT CHAIN; CALPACTIN I, p11 SUBUNIT; CALPACTIN I, LIGHT CHAIN;CELLULAR LIGAND OF ANNEXIN II; S100 calcium-binding protein A10 (annexinII ligand, calpactin I, li S0311 v-myb avian X X X 4605 Hs.179718 MYBL2;MGC15600; MYB-RELATED myeloblastosis viral GENE BMYB; MYB-relatedprotein B; oncogene homolog-like 2 v-myb myeloblastosis viral oncogenehomolog (avian)-like 2; V-MYB AVIAN MYELOBLASTOSIS VIRAL ONCOGENEHOMOLOG-LIKE 2 S0312 nucleoside X 4860 Hs.75514 PNP; NP; EC 2.4.2.1;INOSINE phosphorylase PHOSPHORYLASE; PURINE- NUCLEOSIDE:ORTHOPHOSPHATERIBOSYLTRANSFERASE; purine nucleoside phosphorylase; PNP NUCLEOSIDEPHOSPHORYLASE DEFICIENCY; ATAXIA WITH DEFICIENT CELLULAR IMMUNITY S0314aldo-keto reductase X X 22948 Hs.1600 KIAA0098; CCT5; chaperonin family1, member C4 containing TCP1, subunit 5 (epsilon) S0315 Non-metastaticcells 1, X X X X 4830 Hs.118638 NM23H1; NDPKA; NM23; NM23-H1; protein(NM23A) NME1; NDK A; EC 2.7.4.6; NUCLEOSIDE DIPHOSPHATE KINASE-A; NDPKINASE A; METASTASIS INHIBITION FACTOR NM23; NUCLEOSIDE DIPHOSPHATEKINASE A; non- metastatic cells 1 protein; TUMOR METASTATIC PROCESS-ASSOCIATED PROTEIN; NONMETAS S0316 Squalene epoxidase X X X 6713Hs.71465 SQLE; SE; ERG1; EC 1.14.99.7; squalene epoxidase; squalenemonooxygenase S0319 Pregnancy-induced X 29948 Hs.31773 OKL38;pregnancy-induced growth growth inhibitor inhibitor S0326 Putative 4TMMal-like X X X 114569 Hs.76550 MAL2; MAL proteolipid protein 2; proteinmal, T-cell differentiation protein 2 S0330 aldo-keto reductase X X X1645 Hs.306098 DDH1; 2-ALPHA-HSD; MBAB; 20- family 1, member C1/C2ALPHA-HSD; H-37; DD1; DD2; HBAB; AKR1C1; HAKRC; DDH; EC 1.3.1.20; 20ALPHA- HYDROXYSTEROID DEHYDROGENASE; ALDO-KETO REDUCTASE C; DIHYDRODIOLDEHYDROGENASE 2; DIHYDRODIOL DEHYDROGENASE, TYPE I; CHLORDECONEREDUCTASE HOMOLOG HAKRC S0330x1 aldo-keto reductase X X 1645 Hs.306098DDH1; 2-ALPHA-HSD; MBAB; 20- family 1, member C1/C2 ALPHA-HSD; H-37;DD1; DD2; HBAB; AKR1C1; HAKRC; DDH; EC 1.3.1.20; 20 ALPHA-HYDROXYSTEROID DEHYDROGENASE; ALDO-KETO REDUCTASE C; DIHYDRODIOLDEHYDROGENASE 2; DIHYDRODIOL DEHYDROGENASE, TYPE I; CHLORDECONEREDUCTASE HOMOLOG HAKRC S0331 aldo-keto reductase X X 8644 Hs.78183HA1753; DD3; PGFS; HAKRB; family 1, member C3 KIAA0119; 3ALPHA-HSD;DDH1; AKR1C3; EC 1.3.1.20; 3-ALPHA- HYDROXYSTEROID DEHYDROGENASE;PROBABLE TRANS-1,2-DIHYDROBENZENE- 1,2-DIOL DEHYDROGENASE; ALDO-KETOREDUCTASE B; DIHYDRODIOL DEHYDROGENASE 3; PROSTAGLANDIN F SYNTHASE;3-@ALP S0331x1 aldo-keto reductase X X 8644 Hs.78183 HA1753; DD3; PGFS;HAKRB; family 1, member C3 KIAA0119; 3ALPHA-HSD; DDH1; AKR1C3; EC1.3.1.20; 3-ALPHA- HYDROXYSTEROID DEHYDROGENASE; PROBABLETRANS-1,2-DIHYDROBENZENE- 1,2-DIOL DEHYDROGENASE; ALDO-KETO REDUCTASE B;DIHYDRODIOL DEHYDROGENASE 3; PROSTAGLANDIN F SYNTHASE; 3-@ALP S0332aldo-keto reductase X X 1645 Hs.306098 1.1.1.213; 2-ALPHA-HSD; 1.3.1.20;family 1, member C4 20-ALPHA-HSD; MGC8954; H-37; HAKRC; MBAB; C9; DDH1;AKR1C1; trans-1,2-dihydrobenzene- 1,2-diol dehydrogenase; chlordeconereductase homolog; aldo-keto reductase C; 20 alpha-hydroxysteroiddehydrogenase; hepatic dihydrodiol S0332x1 aldo-keto reductase X X 1645Hs.306098 1.1.1.213; 2-ALPHA-HSD; 1.3.1.20; family 1, member C420-ALPHA-HSD; MGC8954; H-37; HAKRC; MBAB; C9; DDH1; AKR1C1;trans-1,2-dihydrobenzene- 1,2-diol dehydrogenase; chlordecone reductasehomolog; aldo-keto reductase C; 20 alpha-hydroxysteroid dehydrogenase;hepatic dihydrodiol S0336 Putative ParB-like X 140809 Hs.135056 nucleasedomain containing protein S0342 Putative glucose X X X X 154091 Hs.26691GLUT8; GLUT12 transporter-like protein S0343 Putative glucose X X X154091 Hs.26691 GLUT8; GLUT12 transporter-like protein S0374 chlorideintracellular X X X 53405 Hs.283021 CLIC5; chloride intracellularchannel 5 channel 5 (CLIC5), S0380 hypothetical protein X Hs.59509(AX119005 Sequence 169 from Patent WO0129221) S0384 FERM, RhoGEF X 10160Hs.183738 CDEP; FARP1; chondrocyte-derived (ARHGEF) and pleckstrinezrin-like protein; domain protein 1 CHRONDROCYTE-DERIVED(chondrocyte-derived) EZRIN-LIKE PROTEIN; FERM, (FARP1) RhoGEF, andpleckstrin domain protein 1; FERM, ARHGEF, AND PLECKSTRIN DOMAIN-CONTAINING PROTEIN 1; FERM, RhoGEF (ARHGEF) and pleckstrin domainprotein 1 (c S0388 zinc finger transcription X 7227 Hs.26102 TRPS1;factor TRPS1 TRICHORHINOPHALANGEAL (trichorhinophalangeal SYNDROME GENE;syndrome I gene) trichorhinophalangeal syndrome I gene; ZINC FINGERTRANSCRIPTION FACTOR TRPS1 S0398 FAT tumor suppressor X X X 2195Hs.166994 ME5; CDHF7; FAT; cadherin ME5; (Drosophila) homolog FATPROTEIN HOMOLOG; FAT (FAT) tumor suppressor precursor; FAT tumorsuppressor (Drosophila) homolog; cadherin-related tumor suppressorhomolog precursor; cadherin family member 7 precursor; homolog ofDrosophila Fat protein precu S0401 granulin (GRN) X X 2896 Hs.180577ACROGRANIN; PROEPITHELIN; PROGRANULIN; PEPI; PCDGF; granulin; GRN;EPITHELIN PRECURSOR S0404 N-myc downstream X X X X X 10397 Hs.75789NDR1; DRG1; RTP; NDRG1; RIT42; regulated (NDRG1) CAP43; NDRG1 PROTEIN;DIFFERENTIATION-RELATED GENE 1 PROTEIN; NICKEL- SPECIFIC INDUCTIONPROTEIN CAP43; NMYC DOWNSTREAM- REGULATED GENE 1; REDUCING AGENTS ANDTUNICAMYCIN- RESPONSIVE PROTEIN; N-MYC DOWNSTREAM REGULATED GENE 1 PS0411 fatty acid binding protein X 2171 Hs.380942 PAFABP; EFABP; E-FABP;FABP5; 5 (psoriasis- PA-FABP; FATTY ACID-BINDING associated)(FABP5)PROTEIN, EPIDERMAL; FATTY ACID-BINDING PROTEIN 5; FATTY ACID-BINDINGPROTEIN, PSORIASIS-ASSOCIATED; fatty acid binding protein 5 (psoriasis-associated) S0413 cyclin-dependent kinase X 1028 Hs.106070 WBS;p57(KIP2); BWCR; CDKN1C; inhibitor 1C (p57, BWS; Beckwith-WiedemannKip2)(CDKN1C) syndrome; cyclin-dependent kinase inhibitor 1C (p57, Kip2)S0414 alpha-methylacyl-CoA X 23600 Hs.128749 AMACR; 5.1.99.4; ALPHA-racemase(AMACR) METHYLACYL-CoA RACEMASE; AMACR DEFICIENCY; AMACRALPHA-METHYLACYL-CoA RACEMASE DEFICIENCY; alpha- methylacyl-CoA racemaseisoform 1; alpha-methylacyl-CoA racemase isoform 2 S0415gamma-aminobutyric X X 2562 Hs.1440 MGC9051; GABRB3; GABA-A acid (GABA)A receptor, RECEPTOR, BETA-3 beta 3(GABRB3) POLYPEPTIDE; GAMMA-AMINOBUTYRIC ACID RECEPTOR, BETA-3; gamma-aminobutyric acid (GABA) Areceptor, beta 3; gamma- aminobutyric acid (GABA) A receptor, beta 3isoform 2 precursor; gamma-aminobutyric acid (GABA) A rece S0417KIAA0872 protein X X 22879 Hs.436089 KIAA0872 protein (KIAA0872) S0425tumor necrosis factor X 27242 Hs.159651 TNFRSF21; DR6; BM-018; TNFR-receptor superfamily, related death receptor 6; tumor member21(TNFRSF21) necrosis factor receptor superfamily, member 21; tumornecrosis factor receptor superfamily, member 21 precursor S0429 KIAA1380(thyroid X 221037 Hs.381298 JMJD1C; TRIP8; jumonji domain hormonereceptor containing 1C; THYROID interactor 8)(TRIP8) HORMONE RECEPTORINTERACTOR 8 S0432 BSTP5 X X X X Hs.19322 S0440 cell division cycle 25BX X 994 Hs.153752 CDC25HU2; EC 3.1.3.48; M-PHASE (cdc25b) INDUCERPHOSPHATASE 2; DUAL SPECIFICITY PHOSPHATASE CDC25B; cell division cycle25B, isoform 4; cell division cycle 25B, isoform 1; cell division cycle25B, isoform 2; cell division cycle 25B, isoform 3 S0445 laminin, beta 1(lamb1) X 3912 Hs.82124 LAMB1; LAMININ, BETA-1; CUTIS LAXA-MARFANOIDSYNDROME; laminin, beta 1; laminin, beta 1 precursor; LAMB1 NEONATALCUTIS LAXA WITH MARFANOID PHENOTYPE S0447 papillary renal cell X 5546Hs.9629 TPRC; MGC17178; MGC4723; carcinoma (translocation- PRCC;proline-rich protein PRCC; associated) (prcc) RCCP1 PRCC/TFE3 FUSIONGENE; papillary renal cell carcinoma (translocation-associated); RENALCELL CARCINOMA, PAPILLARY, 1 GENE; papillary renal cell carcinomatranslocation-associated gene product S0455 tumor necrosis factor X 8743Hs.83429 APO2L; TL2; Apo-2L; TNFSF10; (ligand) superfamily, Apo-2ligand; APO2 LIGAND; TNF- member 10 (TNFSF10) RELATED APOPTOSIS-INDUCINGLIGAND; TNF-related apoptosis inducing ligand TRAIL; tumor necrosisfactor (ligand) superfamily, member 10; TUMOR NECROSIS FACTOR LIGANDSUPERFAMILY, MEMBER 10 S0459 titin X X 7273 Hs.434384 titin; CMH9; TTN;TTN CARDIOMYOPATHY, FAMILIAL HYPERTROPHIC, 9 S0469 DNA fragmentation X1676 Hs.105658 DFF45; DFF1; DFFA; ICAD; DFF-45; factor, 45 kD, alphaINHIBITOR OF CASPASE- polypeptide ACTIVATED DNase; DNA FRAGMENTATIONFACTOR, 45- KD, ALPHA SUBUNIT; DNA fragmentation factor, 45 kDa, alphapolypeptide; DNA fragmentation factor, 45 kD, alpha subunit; DNAfragmentation factor, 45 kD, alp S0494 Caspase 2 X 835 Hs.108131 CASP-2;ICH-1L/1S; CASP2; ICH1; NEDD2; CASPASE-2 PRECURSOR; ICH-1 protease; EC3.4.22.—; NEDD2 apoptosis regulatory gene; caspase 2, isoform 3; caspase2, isoform 4; caspase 2, isoform 1 preproprotein; caspase 2, isoform 2precursor; NEURAL PRECURSOR CELL S0501 G1 to S phase transition X X X2935 Hs.2707 GSPT1; eRF3a; ETF3A; GST1, 1 (GSPT1) YEAST, HOMOLOG OF;PEPTIDE CHAIN RELEASE FACTOR 3A; G1- TO S-PHASE TRANSITION 1; G1 to Sphase transition 1 S0502 GCN5 general control of X X X 2648 Hs.101067hGCN5; GCN5; GCN5L2; HSGCN5; amino-acid synthesis 5- EC 2.3.1.—; HISTONElike 2 (yeast) (GCN5L2) ACETYLTRANSFERASE GCN5; GENERAL CONTROL OF AMINOACID SYNTHESIS PROTEIN 5-LIKE 2; GCN5 (general control of amino- acidsynthesis, yeast, homolog)-like 2; General control of amino acidsynthesis, yeast, homol S0507 Guanylate-binding X 64225 Hs.27099ARL6IP2; ADP-ribosylation factor-like protein (FLJ23293) 6 interactingprotein 2 S0511 HSPC037 protein X 51659 Hs.433180 Pfs2; DNA replicationcomplex GINS protein PSF2 S0524 Hypothetical protein X 55608 Hs.172572ANKRD10; ankyrin repeat domain 10 (FLJ20093) S0527 Hypothetical proteinX N/A Hs.4935 KCTD2; potassium channel (KIAA0176) tetramerisation domaincontaining 2 S0528 Hypothetical protein X X 23312 Hs.13264 RC3;KIAA0856; rabconnectin-3 (KIAA0856) S0544 RhoGEF containing X 84904Hs.245342 hypothetical protein FLJ14642 hypothetical protein (FLJ14642)S0545 RNA methyltransferase X X X 27037 Hs.63609 D22S1733E; HTF9C; Hpalltiny (Hpall tiny fragments fragments locus 9C; Hpall tiny locus 9C)fragments locus 9C S0546 Serine rich hypothetical X 157313 Hs.373547CDCA2; cell division cycle protein associated 2 S0553 Similar to mitoticX X Hs.180591 phosphoprotein 44 [Xenopus laevis] S0557 SMC4 (structuralX 10051 Hs.50758 CAP-C; SMC4L1; CAPC; HCAP-C; maintenance ofchromosome-associated polypeptide chromosomes 4, yeast)- C; SMC4(structural maintenance of like 1; SMC4L1; CAP-C chromosomes 4,yeast)-like 1; structural maintenance of chromosomes (SMC) familymember, chromosome-associated protein C S0564 phosphatidylserine X X X X9791 Hs.77329 KIAA0024; PSSA; PTDSS1; EC synthase 1 2.7.8.—;phosphatidylserine synthase 1; PHOSPHATIDYLSERINE SYNTHASE I(SERINE-EXCHANGE ENZYME I) (EC 2.7.8.—) S0565 polo (Drosophia)-like X X5347 Hs.433619 PLK1; PLK; PLK-1; STPK13; POLO- kinase LIKE KINASE; EC2.7.1.—; polo (Drosophia)-like kinase; SERINE/THREONINE-PROTEIN KINASEPLK; SERINE-THREONINE PROTEIN KINASE 13; SERINE THREONINE PROTEIN KINASE13 S0567 Pirin X X 8544 Hs.424966 Pirin; PIR S0578 ATP-binding cassette,X 21 Hs.26630 ABCA3; ABC3; LBM180; ABC-C; sub-family A (ABC1),EST111653; ABC transporter 3; member 3 (ABCA3) ATP-binding cassette 3;ATP- BINDING CASSETTE TRANSPORTER 3; ATP-BINDING CASSETTE, SUBFAMILY A,MEMBER 3; ATP-binding cassette, sub-family A member 3; ATP-bindingcassette, sub-family A (ABC1), memb S0579 ATP-binding cassette, X X10347 Hs.134514 ABCX; ABCA7; ABCA-SSN; sub-family A (ABC1), autoantigenSS-N; macrophage ABC member 7 (ABCA7) transporter; ATP-BINDING CASSETTE,SUBFAMILY A, MEMBER 7; ATP-binding cassette, sub-family A (ABC1), member7; ATP-binding cassette, sub-family A, member 7, isoform a; ATP-bindingcassette, sub-famil S0581 ATP-binding cassette, X X X 22 Hs.125856ABCB7; Atm1p; ASAT; ABC7; sub-family B (MDR/TAP), EST140535; ABCTRANSPORTER member 7 (ABCB7) 7; ATP-binding cassette 7; ATP- BINDINGCASSETTE TRANSPORTER 7; Anemia, sideroblastic, with spinocerebellarataxia; ATP-BINDING CASSETTE, SUBFAMILY B, MEMBER 7; ATP- bindingcassette, sub-family B, member S0585 ATP-binding cassette, X X 94160Hs.343663 MRP9; ABCC12; ATP-binding sub-family C cassette, sub-family C,member 12; (CFTR/MRP), member ATP-binding cassette, sub-family C 12(ABCC12) (CFTR/MRP), member 12 S0586 ATP-binding cassette, X X 9429Hs.194720 ABC15; MXR1; ABCP; EST157481; sub-family G (WHITE), MXR;ABCG2; ABC1; BCRP; member 2 (ABCG2) mitoxantrone resistance protein;breast cancer resistance protein; placenta specific MDR protein; ATP-BINDING CASSETTE TRANSPORTER, PLACENTA- SPECIFIC; ATP-BINDING CASSETTE,SUBFAMILY G, MEMBER 2; ATP-b S0593 solute carrier family 21 X X X X28234 Hs.274981 SLC21A8; OATP8; ORGANIC (organic anion ANION TRANSPORTER8; SOLUTE transporter), member 8 CARRIER FAMILY 21, MEMBER 8; (SLC21A8)solute carrier family 21 (organic anion transporter), member 8 S0597solute carrier family 22 X 9356 Hs.23965 ROAT1; MGC45260; HOAT1; PAHT;(organic anion SLC22A6; PAH TRANSPORTER; transporter), member 6para-aminohippurate transporter; (SLC22A6) renal organic aniontransporter 1; solute carrier family 22 member 6 isoform b; solutecarrier family 22 member 6 isoform c; solute carrier family 22 member 6isoform S0604 solute carrier family 35 X X X 7355 Hs.21899 UGT2; UGTL;UGAT; SLC35A2; (UDP-galactose UGT1; UDP-galactose translocator;transporter), member 2 UDP-GALACTOSE (SLC35A2) TRANSPORTER, ISOFORM 2;UGALT UDP-GALACTOSE TRANSPORTER, ISOFORM 1; solute carrier family 35(UDP- galactose transporter), member A2; solute carrier family 35 (UDP-galactose transpo S0607 cdc25b isoforms 1,3 X 994 Hs.153752 CDC25HU2; EC3.1.3.48; M-PHASE INDUCER PHOSPHATASE 2; DUAL SPECIFICITY PHOSPHATASECDC25B; cell division cycle 25B, isoform 4; cell division cycle 25B,isoform 1; cell division cycle 25B, isoform 2; cell division cycle 25B,isoform 3 S0609 SCD stearoyl-CoA X 6319 Hs.119597 SCD;DELTA(9)-DESATURASE; desaturase delta-9- ACYL-COA DESATURASE; ECdesaturase 1.14.99.5; stearoyl-CoA desaturase (delta-9-desaturase);FATTY ACID DESATURASE S0611 MAPK12 mitogen- X X 6300 Hs.55039 SAPK3;p38gamma; SAPK-3; ERK6; activated protein kinase MAPK12; p38-GAMMA;PRKM12; 12 ERK-6; ERK3; ERK5; EC 2.7.1.—; STRESS-ACTIVATED PROTEINKINASE-3; mitogen-activated protein kinase 3; EXTRACELLULARSIGNAL-REGULATED KINASE 6; MAP KINASE P38 GAMMA; stress- activatedprotein kinase S0612 NFKB2 nuclear factor of X X 4791 Hs.73090 LYT-10;LYT10; NFKB2; kappa light 2 (p49/p100) ONCOGENE LYT 10; TRANSCRIPTIONFACTOR NFKB2; NFKB, p52/p100 SUBUNIT; LYMPHOCYTE TRANSLOCATIONCHROMOSOME 10; NUCLEAR FACTOR KAPPA-B, SUBUNIT 2; Nuclear factor ofkappa light chain gene enhancer in B-cells 2; nuclear factor of kappa IS0613 TNFRSF5: tumor X X 958 Hs.25648 Bp50; TNFRSF5; MGC9013; necrosisfactor receptor CDW40; CD40 antigen; CD40L superfamily, member 5receptor; B CELL-ASSOCIATED (CD40) MOLECULE CD40; CD40 type II isoform;B cell surface antigen CD40; nerve growth factor receptor-relatedB-lymphocyte activation molecule; tumor necrosis factor receptorsuperfam S0614 EBI3 Epstein-Barr virus X X X 10148 Hs.185705 EBI3;EPSTEIN-BARR VIRUS- induced gene 3 INDUCED GENE 3; Epstein-Barr virusinduced gene 3 precursor S0616 ZNF339: zinc finger X 58495 Hs.71935ZNF339; zinc finger protein 339 protein 339 S0617 DAB2IP: DAB2 X 153090Hs.238465 DAB2IP; DAB2 interacting protein interacting protein S0618PPFIA1: protein tyrosine X 8500 Hs.183648 MGC26800; LIP1; PPFIA1; LIP.1;phosphatase, receptor LAR-interacting protein 1; PTPRF type, fpolypeptide interacting protein alpha 1 isoform a; (PTPRF), interactingPTPRF interacting protein alpha 1 protein (liprin), alpha 1 isoform b;protein tyrosine phosphatase, receptor type, f polypeptide (PTPRF),interacting protein (liprin), alpha 1 S0631 hypothetical protein X 56963Hs.271277 RGMA; REPULSIVE GUIDANCE CAB66760 MOLECULE; RGM domain family,member A S0633 novel hypothetical protein X X 144347 Hs.432901LOC144347; hypothetical protein LOC144347 S0645 frizzled homolog 7 FZD7X 8324 Hs.173859 FzE3; FZD7; frizzled 7; frizzled homolog 7(Drosophila); Frizzled, drosophila, homolog of, 7 S0646 SLC3A2: solutecarrier X X 6520 Hs.79748 MDU1; 4T2HC; SLC3A2; NACAE; family 3, member 24F2HC; 4F2 HEAVY CHAIN; CD98 HEAVY CHAIN; CD98 MONOCLONAL ANTIBODY 44D7;ANTIGEN DEFINED BY MONOCLONAL ANTIBODY 4F2, HEAVY CHAIN; antigenidentified by monoclonal antibodies 4F2, TRA1.10, TROP4, and T43; SOLUTECARRIER FAMILY 3 S0651 PLA2R1 phospholipase X 22925 Hs.171945 PLA2IR;PLA2-R; PLA2R1; A2 receptor 1 PLA2G1R; PHOSPHOLIPASE A2 RECEPTOR,180-KD; phospholipase A2 receptor 1, 180 kDa S0654 KIAA0182 protein X23199 Hs.222171 KIAA0182; KIAA0182 protein (KIAA0182) S0659 thymidinekinase 2, X 7084 Hs.274701 TK2; THYMIDINE KINASE, mitochondrialMITOCHONDRIAL; thymidine kinase 2, mitochondrial S0663 hypotheticalprotein X X X 64430 Hs.413671 FLJ12799; hypothetical protein FLJ12799(FLJ12799) FLJ12799 S0665 KIAA1007 protein X X X 23019 Hs.279949KIAA1007; KIAA1007 protein; (KIAA1007) adrenal gland protein AD-005;KIAA1007 protein isoform a; KIAA1007 protein isoform b S0670DKFZP566O1646 protein X 25936 Hs.24427 DC8; DKFZP566O1646 protein (DC8)S0673 ART-4 protein (ART-4) X 28987 Hs.3566 ART-4; NOB1P; adenocarcinomaantigen recognized by T lymphocytes 4; likely ortholog of mouse nin onebinding protein S0676 guanine nucleotide X 2768 Hs.182874 RMP; NNX3;GNA12; G alpha 12; binding protein (G Guanine nucleotide-bindingprotein, protein) alpha 12 alpha-12 subunit; guanine nucleotide (GNA12)binding protein (G protein) alpha 12 S0677 GrpE-like protein X 80273Hs.151903 HMGE; GrpE-like protein cochaperone (HMGE) cochaperone; HUMANMITOCHONDRIAL GrpE PROTEIN; GrpE, E. COLI, HOMOLOG OF S0684 FLJ34922 X91607 Hs.235709 S0687 hypothetical protein X 54942 Hs.29276 FLJ20457;hypothetical protein FLJ20457 FLJ20457 S0691 solute carrier family 7, X23657 Hs.6682 CCBR1; SLC7A11; xCT; (cationic amino acidcystine/glutamate transporter; transporter, y+ system) SYSTEM Xc(—)TRANSPORTER- member 11 RELATED PROTEIN; SOLUTE CARRIER FAMILY 7, MEMBER11; solute carrier family 7, (cationic amino acid transporter, y+system) member 11 S0695 Integrin, beta 4 X 3691 Hs.85266 GP150; ITGB4;CD104 antigen; INTEGRIN, BETA-4; Integrin beta-4 precursor; integrin,beta 4 S0702 Solute Carrier Family 7, X X 8140 Hs.184601 SLC7A5; MPE16;D16S469E; E16; member 5/LAT1 protein 4F2LC; CD98; HLAT1; LAT1; CD98LC;4F2 LC; 4F2 light chain; CD98 LIGHT CHAIN; INTEGRAL MEMBRANE PROTEINE16; L-TYPE AMINO ACID TRANSPORTER 1; Solute carrier family 7, member 5;LARGE NEUTRAL AMINO ACIDS TRANSPORTER SMALL SUBUN S5002 cytokeratin 14 XX X 3861 Hs.355214 CK; KRT14; K14; EBS4; EBS3; cytokeratin 14; CK 14;KERATIN, TYPE I CYTOSKELETAL 14; keratin 14 (epidermolysis bullosasimplex, Dowling-Meara, Koebner) S5003 cytokeratin 17 X 3872 Hs.514738PCHC1; PC; PC2; 39.1; KRT17; K17; CYTOKERATIN 17; VERSION 1; CK 17;KERATIN, TYPE I CYTOSKELETAL 17 S5004 cytokeratin 18 X 3875 Hs.406013K18; CYK18; KRT18; CYTOKERATIN 18; CK 18; KERATIN, TYPE I CYTOSKELETAL18 S5005 CAM5.2 (cytokeratin X X X 3875 Hs.65114 K18; CYK18; KRT18;8/18) CYTOKERATIN 18; CK 18; KERATIN, TYPE I CYTOSKELETAL 18 S5012 EpCAM(Ab-1), mono X X X 4072 Hs.692 TROP1; LY74; Ep-CAM; GA733-2; EGP40;MK-1; CO17-1A; EPCAM; M4S1; KSA; TACSTD1; EGP; MK-1 antigen; EPITHELIALCELLULAR ADHESION MOLECULE; GASTROINTESTINAL TUMOR- ASSOCIATED ANTIGEN2, 35-KD GLYCOPROTEIN; tumor-associated calcium signal transducer 1precurso S5014 Estrogen Receptor Beta X 2100 Hs.103504 ER-BETA;ESR-BETA; ESR2; Erb; (Ab-2), mono ESRB; NR3A2; ESTROGEN RECEPTOR, BETA;estrogen receptor 2 (ER beta) S5038 milk fat globule protein X X X 4582Hs.89603 PEMT; MUC1; episialin; EMA; PUM; (C-Mu1) H23AG; CD227; PEM;CARCINOMA-ASSOCIATED MUCIN; H23 antigen; TUMOR- ASSOCIATED MUCIN; DF3antigen; peanut-reactive urinary mucin; mucin 1, transmembrane;polymorphic epithelial mucin; MUCIN 1, URINARY; MUCIN, TUMOR- ASSOCIATES5044 CD 71 MAB-3 X X X 7037 Hs.77356 P90; TR; TFRC; TFR; CD71; T9;TRFR; ANTIGEN CD71; TRANSFERRIN RECEPTOR PROTEIN; transferrin receptor(p90, CD71) S5045 c-ErbB2/Her-2 X X 2064 Hs.323910 HER-2; ERBB2; NGL;P185ERBB2; HER2; C-ERBB-2; NEU; MLN 19; EC 2.7.1.112; TKR1 HERSTATIN;NEU PROTO-ONCOGENE; ONCOGENE ERBB2; RECEPTOR PROTEIN- TYROSINE KINASEERBB-2 PRECURSOR; ONCOGENE NGL, NEUROBLASTOMA- OR GLIOBLASTOMA-DERIVED;TYROSINE KINASE-TYPE CELL S5047 LRP/MVP MAB-2 X X X 9961 Hs.80680 MVP;LRP; VAULT1; LUNG RESISTANCE-RELATED PROTEIN; MAJOR VAULT PROTEIN, RAT,HOMOLOG OF S5064 TP63 X 8626 Hs.137569 LMS; TP73L; KET; SHFM4; p73H;EEC3; TP63; p51; TUMOR PROTEIN p63; TUMOR PROTEIN p73-LIKE; p53-RELATEDPROTEIN p63; tumor protein 63 kDa with strong homology to p53 S5065estrogen receptor 1 X 2099 Hs.1657 ER; NR3A1; ESR1; Era; ESR; ER- ALPHA;ESRA; ESTRADIOL RECEPTOR; ESTROGEN RECEPTOR, ALPHA; estrogen receptor 1(alpha) S5066 c-ErbB2/Her-2 X X 2064 Hs.446352 HER-2; ERBB2; NGL;P185ERBB2; HER2; C-ERBB-2; NEU; MLN 19; EC 2.7.1.112; TKR1 HERSTATIN;NEU PROTO-ONCOGENE; ONCOGENE ERBB2; RECEPTOR PROTEIN- TYROSINE KINASEERBB-2 PRECURSOR; ONCOGENE NGL, NEUROBLASTOMA- OR GLIOBLASTOMA-DERIVED;TYROSINE KINASE-TYPE CELL S5067 Cathepsin D X X X 1509 Hs.343475 CTSD;MGC2311; CPSD; EC 3.4.23.5; cathepsin D preproprotein; Cathepsin Dprecursor; cathepsin D (lysosomal aspartyl protease); S5069 CA 125 X94025 Hs.432676 S5070 CA 15-3 X X X N/A N/A S5071 CA 19-9 X X X N/A N/AS5072 c-myc X X 4609 Hs.79070 c-Myc; MYC; ONCOGENE MYC; Mycproto-oncogene protein; PROTOONCOGENE HOMOLOGOUS TO MYELOCYTOMATOSISVIRUS; v- myc myelocytomatosis viral oncogene homolog (avian); v-mycavian myelocytomatosis viral oncogene homolog; Avian myelocytomatosisviral (v-myc) onco S5073 e-cadherin X X X 999 Hs.194657 CDH1;Cadherin-1; Arc-1; ECAD; CDHE; Uvomorulin; LCAM; Epithelial-cadherinprecursor; cell- CAM 120/80; CADHERIN, EPITHELIAL; calcium-dependentadhesion protein, epithelial; cadherin 1, E-cadherin (epithelial);cadherin 1, type 1 preproprotein; cadherin 1, S5074 gst-pi X X 2950Hs.226795 GSTP1; DFN7; GSTP1-1; GST3; GSTPP; GST class-pi; glutathionetransferase; EC 2.5.1.18; glutathione S-transferase pi; GST, CLASS PI;deafness, X-linked 7; GLUTATHIONE S-TRANSFERASE 3; GLUTATHIONE S-TRANSFERASE, PI; FAEES3 GLUTATHIONE S-TRANSFERASE PI PSEUD S5075 p53 X X7157 Hs.1846 p53; TP53; TRP53; PHOSPHOPROTEIN P53;TRANSFORMATION-RELATED PROTEIN 53; TUMOR SUPPRESSOR P53; CELLULAR TUMORANTIGEN P53; tumor protein p53 (Li-Fraumeni syndrome) S5076 progesteronereceptor X X 5241 Hs.2905 NR3C3; PR; PGR; PROGESTERONE RESISTANCE;PSEUDOCORPUS LUTEUM INSUFFICIENCY PROGESTERONE RECEPTOR S5077 ps2 MAB-1X 7031 Hs.350470 S5079 NSE X X X 2026 Hs.511915 NSE; ENO2;2-phospho-D-glycerate hydro-lyase; ENOLASE, GAMMA; neurone-specificenolase; ENOLASE, NEURON-SPECIFIC; 2- phospho-D-glycerate hydrolyase; EC4.2.1.11; Neural enolase; enolase-2, gamma, neuronal; neuron specificgamma enolase; enolase 2, (gamma, S5080 bcl-2 X 596 Hs.79241 BCL2;FOLLICULAR LYMPHOMA; APOPTOSIS REGULATOR BCL-2; B-cell CLL/lymphoma 2;B-cell lymphoma protein 2 alpha; B-cell lymphoma protein 2 beta;ONCOGENE B-CELL LEUKEMIA 2 LEUKEMIA, CHRONIC LYMPHATIC, TYPE 2 S5081 RBX X 5925 Hs.75770 p105-Rb; PP110; Retinoblastoma-1; RB; RB1;RETINOBLASTOMA- ASSOCIATED PROTEIN; RB OSTEOSARCOMA,RETINOBLASTOMA-RELATED; retinoblastoma 1 (including osteosarcoma) S5082Synaptophysin X X 6855 Hs.75667 SYP; Synaptophysin; Major synapticvesicle protein P38 S5083 bax X 581 Hs.159428 BAX; BCL2-associated Xprotein; APOPTOSIS REGULATOR BAX, MEMBRANE ISOFORM ALPHA S6001 estrogenreceptor X 2099 Hs.1657 ER; NR3A1; ESR1; Era; ESR; ER- ALPHA; ESRA;ESTRADIOL RECEPTOR; ESTROGEN RECEPTOR, ALPHA; estrogen receptor 1(alpha) S6002 progesterone receptor X 5241 Hs.2905 NR3C3; PR; PGR;PROGESTERONE RESISTANCE; PSEUDOCORPUS LUTEUM INSUFFICIENCY PROGESTERONERECEPTOR S6003 c-ErbB2/Her-2 X 2064 Hs.323910 HER-2; ERBB2; NGL;P185ERBB2; HER2; C-ERBB-2; NEU; MLN 19; EC 2.7.1.112; TKR1 HERSTATIN;NEU PROTO-ONCOGENE; ONCOGENE ERBB2; RECEPTOR PROTEIN- TYROSINE KINASEERBB-2 PRECURSOR; ONCOGENE NGL, NEUROBLASTOMA- OR GLIOBLASTOMA-DERIVED;TYROSINE KINASE-TYPE CELL S6004 bcl-2 X 596 Hs.79241 BCL2; FOLLICULARLYMPHOMA; APOPTOSIS REGULATOR BCL-2; B-cell CLL/lymphoma 2; B-celllymphoma protein 2 alpha; B-cell lymphoma protein 2 beta; ONCOGENEB-CELL LEUKEMIA 2 LEUKEMIA, CHRONIC LYMPHATIC, TYPE 2 S6005 cytokeratin5/6 X X 3852 Hs.433845 KRT5; EBS2; Keratin-5; K5; CYTOKERATIN 5; CK 5;58 KDA CYTOKERATIN; KERATIN, TYPE II CYTOSKELETAL 5; keratin 5(epidermolysis bullosa simplex, Dowling-Meara/Kobner/Weber- Cockaynetypes) S6006 p53 X X 7157 Hs.1846 p53; TP53; TRP53; PHOSPHOPROTEIN P53;TRANSFORMATION-RELATED PROTEIN 53; TUMOR SUPPRESSOR P53; CELLULAR TUMORANTIGEN P53; tumor protein p53 (Li-Fraumeni syndrome) S6007 ki67 X X4288 Hs.80976 ki67; MKI67 S6008 EGFR 1 X X 1956 Hs.77432 S7; EGFR;2.7.1.112; ERBB; ONCOGENE ERBB; ERBB1 SPECIES ANTIGEN 7; V-ERB-B AVIANERYTHROBLASTIC LEUKEMIA VIRAL ONCOGENE HOMOLOG; epidermal growth factorreceptor (avian erythroblastic leukemia viral (v-erb-b) oncogenehomolog) S6011 NSE X 2026 Hs.146580 NSE; ENO2; 2-phospho-D-glyceratehydro-lyase; ENOLASE, GAMMA; neurone-specific enolase; ENOLASE,NEURON-SPECIFIC; 2- phospho-D-glycerate hydrolyase; EC 4.2.1.11; Neuralenolase; enolase-2, gamma, neuronal; neuron specific gamma enolase;enolase 2, (gamma, S6012 Thyroid Transcription X 7080 Hs.94367 benignchorea; chorea, hereditary Factor-1 benign; NK-2 (Drosophila) homolog A(thyroid nuclear factor); Thyroid transcription factor 1 (NK-2,Drosophila, homolog of, A); BCH; BHC; TEBP; TTF1; NKX2A; TTF-1; NKX2.1S6013 c-ErbB2/Her-2 X 2064 Hs.323910 HER-2; ERBB2; NGL; P185ERBB2; HER2;C-ERBB-2; NEU; MLN 19; EC 2.7.1.112; TKR1 HERSTATIN; NEU PROTO-ONCOGENE;ONCOGENE ERBB2; RECEPTOR PROTEIN- TYROSINE KINASE ERBB-2 PRECURSOR;ONCOGENE NGL, NEUROBLASTOMA- OR GLIOBLASTOMA-DERIVED; TYROSINEKINASE-TYPE CELL

Antibody Generation IHC Images Peptide 1 Peptide 3 (SEQ ID (Appendix B)AGI ID (SEQ ID NO:) Peptide 2 (SEQ ID NO:) NO:) TITER Breast IHC LungIHC Colon IHC S0011 DYISKSKEDVK EKRTNGLRRTPKQVD TEESINDEDIYKGL 1:90-1:100 LK (1) (2) PDLIDE (3) S0018 SKTINPQVSKT DDNATTNAIDELKEC (5)NQTDETLSNVEVF 1:300 EYKELLQE (4) MQ (6) S0020 SSDDGIRPLP NKTKKKKSSRLPPEKDGKSKDKPPKRKK 1:100 EYSTEKHKK (8) ADTE (9) (7) S0021 KNKEPLTKKGKLTCTDLDSSPRSFRYS EVDYENPSNLAAG  1:200-1:2500 2_25_2_5_66_21_58 ETKTAERD(11) NKYT (12) (10) S0022 KTLQVFNPLR QHFAIIECKVAVALT (14) RKFLAPDHSRPPQ 1:50-1:500 1_25_12_6_67_22_73 1_26_5_2_72_22_24 1_32_14_2_89_22_15FSRENSEKIH PVRQ (15) (13) S0024 VLPLKNILDEIK YELCREVRRRRMVQGMAKSAEVKLAIFG  1:900-1:1000 KPKN (16) KT (17) RAGVGK (18) S0032KNTEISFKLGV HLQKWDGQETTLVRE TKPTTIIEKNGDILT 1:225 EFDE (19) (20) LKTH(21) S0036 LQQMAAKDR KRKISFASIEISSDNVDY DGNDVEFTWLRG 1:250-1:500GTTKEVEEVS SD (23) NDSVRGLEH (24) (22) S0037 QRQQIAKSFKREIMKAYEEDYGSSLEE EEYEKIANKSIEDSI 1:30-1:40 AQFGKDLTE DIQ (26) KSE (27)(25) S0039 EGGSLVPAAR RKAGKSKKSFSRKEAE KTHEKYGWVTPPV    1:50-1:300003_18_4_3_108_39_26 3_26_6_4_67_39_51 QQHCTQVRS (29) SDG (30) RR (28)S0040 MDLEGDRNG NLEDLMSNITNRSDIND RGSQAQDRKLSTK 1:200-1:4003_18_4_6_63_40_63 2_26_3_5_129_40_59 1_32_6_3_105_40_34 GAKKKN (31) TG(32) EA (33) S0041 MDLEAAKNGT NFSFPVNFSLSLLNPGK KNSQMCQKSLDV  1:60-1:3002_18_7_4_102_41_54 AWRPTSAE (35) ETDG (36) (34) S0042 MALRGFCSADKNWKKECAKTRKQPVK DSIERRPVKDGGG  1:40-1:500 1_18_11_3_65_42_332_26_3_5_176_42_59 4_32_8_6_170_42_77 GSD (37) (38) TNS (39) S0043MLEKFCNSTF SILCGTFQFQTLIRT (41) ENNESSNNPSSIA  1:50-1:3331_18_11_3_66_43_33 3_26_12_8_177_43_101 4_32_8_6_171_43_77 WNSSFLDSPE S(42) (40) S0044 QEVKPNPLQD DEISQRNRQLPSDGKK VQDFTAFWDKASE  1:20-1:1002_18_7_10_67_44_144 2_26_8_3_169_44_36 4_32_8_6_167_44_77 ANICSR (43)(44) TPTLQ (45) S0045 MDALCGSGEL RKQEKQTARHKASAA DPQSVERKTISPG  1:2000GSKFWDSN (47) (48) (46) S0046 MKDIDIGKEYII RDREDSKFRRTRPLECSKHESSDVNCRRL 1:100-1:300 1_18_5_5_68_46_49 1_26_14_8_134_46_994_32_8_6_120_46_77 PSPGYRS (49) QD (50) ER (51) S0047 MAAPAEPCAGDPGVVDSSSSGSAAGK HTLVAENAMNAEK 1:50  3_25_5_3_118_47_333_26_10_4_173_47_47 4_32_8_6_169_47_77 QGVWNQTEP D (53) (54) E (52)S0048 QEVLSKIQHG TNSSLNQNMTNGTR MSDSVILRSIKKFG 1:600 HTIIS (55) (56)EEND (57) S0049 GADDPSSVTA NAVASPEFPPRFNT KPNGIYRKLMNKQ 1:10-1:252_18_3_8_71_49_118 EEIQR (58) (59) SFISA (60) S0050 MASSRCPAPRQGGSGNPVRR (62) EFVGDGIYNNTMG 1:80  3_18_11_7_103_50_77 GCR (61) HVHS(63) S0052 MPLAFCGSEN DHLGKENDVFQPKTQF EIREEQCAPHEPT  1:25-1:1501_18_2_8_74_52_87 HSAAYR (64) LG (65) PQG (66) S0053 MSLSFCGNNIQRVNETQNGTNNTTGI DEIGDDSWRTGES 1:25-1:50 3_18_3_7_75_53_69 SS (67) SE(68) SLPFES (69) S0055 MVKVTFNSAL QTIEENIKIFEEEEVE HDKETYKLQRRET1:450-1:500 AQKEAKKDEP (71) IKGIQKRE (72) K (70) S0057 HKKETESDQDEGFKVKTKKEIRHVEKK MAHAASQLKKNR 1:750 DEIEKTDRRQ SHS (74) DLEINAEE (75)(73) S0058 ERALAAAQRC TAGMKDLLSVFQAYQ DPPRTVLQAPKEW 1:20  HKKVMKER (77)VCL (78) (76) S0059 MEAADASRSN ELHLKPHLEGAAFRDH EGEGLGQSLGNFK  1:50-1:3000 2_18_2_5_41_59_62 2_26_13_1_128_59_13 4_32_8_6_166_59_77GSSPEARDAR Q (80) DDLLN (81) (79) S0059P2 ELHLKPHLEG N/A N/A 1:90 AAFRDHQ (82) S0063 GSEERGAGR KIWSLAETATSPDNPRR KKLLKTAFQPVPR 1:200-1:1200 GSSGGREE S (84) RPQNHLD (85) (83) S0068 RRSSTTHVKQ N/A N/A  1:500-1:40000 AINKMLTKISS (86) S0070 MRRKNTCQNF NETILYFPFSSHSSYTVKVQIPAYIEMNIPL  1:10-1:100 MEYFCISLAF RSKK (88) VILCQ (89) (87) S0072MLTELEKALN RDDLKKLLETECPQYIR KMGVAAHKKSHE  1:6500-1:10000 SIIDVYHK (90)KKGAD (91) ESHKE (92) S0073 PESRKDPSGA HGLAPHESQLHLKGD EQQHKLDFKAYEQ 1:100-1:2700 SNPSADS (93) (94) ALQYS (95) S0073P2 PESRKDPSGAHGLAPHESQLHLKGD EQQHKLDFKAYEQ  1:50-1:450 SNPSADS (96) (97) ALQYS (98)S0074 EEYVGLSANQ RVDCGYPHVTPKECN VPWCFKPLQEAEC  1:2500-1:300001_25_8_4_98_74_49 2_26_4_3_68_74_32 CAVPAKDRVD (100) TF (101) (99)S0074P3 VPWCFKPLQE N/A N/A 1:810 AECTF (102) S0076x1 KKEPVTTRQVQDGKVISSREQVHQTT SSSIKGSSGLGGG 1:200 RTIVEE (103) R (104) SS (105) S0079DHNHAASGKN EEPAMEMKRGPLFSHL QRYSREELKDAGV 1:400-1:800 KRKALCPDHD SSQNI(107) ATL (108) (106) S0081 MDIEAYLERIG QMWQPLELISGKDQPQ FNISLQRKLVPKH1:10-1:60 1_18_5_5_55_81_49 YKKSRNKLDL VPCVFR (110) GDRFFTI (111) E(109) S0086 QPPFLCQWG EKTHGLVVENQELRQR RQRLTHLSPEEKA 1:180-1:400RHQPSWKPL LGMD (113) LRRKLKNR (114) MN (112) S0096 REHMQAVTRNKKSKAVLDYHDDN DEFYSREGRLQDL 1:100-1:800 YITHPR (115) (116) APDTAL (117)S0110 RYAFDFARDK SVFYQYLEQSKYRVMN EDGAWPVLLDEFV  1:500-1:25001_25_14_7_92_110_98 1_26_4_7_207_110_88 4_32_8_6_207_110_77 DQRSLDID KDQ(119) EWQKVRQTS (118) (120) S0117 SFKSPQVYLK RKKQQEAQGEKASRYIEDIGITVDTVLILEE 1:200 EEEEKNEKR E (122) KEQTN (123) (121) S0119DFRCEEGQEE DGTSFAEEVEKPTKCG KAFRGATDLKNLR 1:900 GGCLPRPQ CALCA (125)LDKNQ (126) (124) S0132 MNLLDPFMKM NTFPKGEPDLKKESEE KNGQAEAEEATEQ1:100-1:500 4_25_6_1_95_132_6 1_26_4_8_127_132_109 TDEQEKGLS DK (128)THISPN (129) (127) S0137 QASSLRLEPG ELKGFAERLQRNESGL RSGKSQPSYIPFLL1:2000-1:5000 4_25_14_3_51_137_42 1_26_3_1_65_137_3 1_32_5_3_80_137_33RANDGDWH DSGR (131) REE (132) (130) S0139 RRSDYAKVAK KNFTMNEKLKKFFNVLTEFFVNEARKNNHH  1:2500-1:30000 IFYNLSIQSFD TN (134) FKSESEE (135) D (133)S0140 KNTQAAEALV QENQPENSKTLATQLN KQMEKDLAFQKQ   1:250-1:200002_25_5_5_70_140_61 KLYETKLCE Q (137) VAEKQLK (138) (136) S0140P1KNTQAAEALV N/A N/A 1:270 KLYETKLCE (136) S0143 EFVEQLRKEGDRHPQALEAAQAELQQ REVRQLTLRKLQE  1:5000-1:30000 2_25_12_8_64_143_101VFAKEVR HD (140) LSSKADE (141) (139) S0143P3 EFVEQLRKEG DRHPQALEAAQAELQQREVRQLTLRKLQE 1:300-1:630 VFAKEVR HD (140) LSSKADE (141) (139) S0144AYIREGHEKQ DEASLEPGYPKHIKELG RGSFMGSDEVFTY   1:500-1:200002_25_13_7_54_144_97 2_26_12_7_208_144_96 ADIMIFFAE R (143) FYK (144)(142) S0149 RQEHCMSEHF QGHKWGESPSQGTQA RACGKRVSEGDR   1:400-1:20000KNRPACLGAR GAGK (146) NGSGGGKWG (145) (147) S0156 MVEAFCATWKQVGNVTKPTVIISQE KVVIRTLSTFKNTE   1:100-1:20000 LTNSQN (148) (149) (150)S0158 RAVFREAEVT QEPALFSTDNDDFTVR QKYEAHVPENAVG 1:150-1:5001_18_9_10_8_158_102 1_26_3_8_69_158_110 1_32_2_1_94_158_2 LEAGGAEQE N(152) HE (153) (151) S0165 KKIIEKMLNSD N/A N/A 1:100-1:500 KSN (154)S0171 GKPGNQNSK QAEAPLVPLSRQNK NCFLTERKAQPDE 1:22500-1:30000 NEPPKKRERE(156) (157) R (155) S0193 KQVDLENVWL EFDTVDLSAVDVHPN NKEVYHEKDIKVFF  1:20000 DFIRE (158) (159) DKAK (160) S0211 KRGIEERIQEEDRVIGKNRQPKFEDRT NPQHFLDDKGQFK  1:500-1:2500 SGFLIE (161) K (162) KSD(163) S0218 RHCILGEWLPI KQRELAGNTMTVSYMS RNAHGSCLHASTA 1:20-1:504_25_8_5_73_218_64 LIMAVFN (164) (165) NGSILAGL (166) S0221 ELMEKEVEPEKARSFCKTHARLFKK KNKRLSGMEEWIE  1:500-1:1200 GSKRTD (167) (168) GEK (169)S0223 EGSTDLPLAP KVAQQQRHLEKQHLR DHKHLDHEVAKPA    1:30-1:100001_32_8_3_135_223_36 ESRVDPE (171) RRKRLPE (172) (170) S0235 KLTIESTPFNVKSDLVNEEATGQFRVY KPVEDKDAVAFTC  1:500-1:4500 AEGKEC (173) PELPK (174)EPEAQ (175) S0237 DEKLISLICRA KDKWDELKEAGVSDM DSWIVPLDNLTKD  1:1000VKATFNPAQD KLGD (177) DLDEEEDTHL K (176) (178) S0241 RKRVLEAKELRHGVSHKVDDSSGSIG EARYPLFEGQETG  1:500-1:7500 1_26_7_3_133_241_354_32_8_6_168_241_77 ALQPKDDIVD RRYAR (180) KKETIEE (181) (179) S0251EALYPQRRSY DYYKVPRERRSSTAKP DKYDVPHDKIGKIF  1:5400 2_32_5_6_134_251_80TSEDEAWK EVE (183) KKCKK (184) (182) S0253 DPDQYNFSSS EYIRQLPPNFPYRDDDTTVLLPPYDDAT  1:500-1:2000 ELGGDFEFMD (186) VNGAAKE (187) D (185) S0255RREEVTKKHQ KESRYVHDKHFEVLHS DFFDRFMLTQKDI 1:1000-1:2000 YEIR (188) DLE(189) NK (190) S0260 ESKHFTRDLM ETDRLPRCVRSTARLA ESRWKDIRARIFLI1:3600-1:5400 EKLKGRTSR R (192) ASKELE (193) (191) S0265 SEWRSSGEQKCKCKFGQKSGHHPG KVTLGLLVFLAGFP 1:400 AGR (194) E (195) VLDANDLED (196)S0267 KVAKESDSVF EREKTVTGEFIDKESKR KRAEDTAGQTALT 1:200-1:2502_26_9_1_205_267_9 VLKIYHLRQED PK (198) VMRPD (199) (197) S0270KVARKVRALY ETEVAAVDKLNVIDDDV EIKKSEPEPVYIDE 1:1000-1:90002_26_8_3_200_270_36 DFEAVEDNE E (201) DKMDR (202) (200) S0273 DEECGTDEYCRGEIEETITESFGNDHS N/A 1:400-1:500 ASPTRGGD TLD (204) (203) S0280MDLRRRDYH DTDIYRDVAEYSEAKE EFYSDALKQRCGV 1:1800-1:24002_25_6_1_108_280_6 MERPLLNQEH (206) DVDFLISQKKK LEE (205) (207) S0286DAHQARVLIG ERRICECPDGFHGPHC N/A 1:90  FEEDILIVSE EK (209) (208) S0288DIKMILKMVQL KRKVDGLSTEAEQPFIP KEGACDELFSYLIE  1:1200 DSIEDLE (210) VE(211) KVKRKK (212) S0295 RLRKKAFANP RSDPDVERCLRAHRND RVAHTVAYLGKLR 1:100-1:2400 EDALR (213) (214) APIR (215) S0296 KRRALAAPAAEAREKMLAAKSADGSA MIWLRHRKPELER  1:300-1:5000 1_25_8_4_56_296_493_26_13_6_54_296_72 4_32_8_6_90_296_77 EEKEEAR PAGE (217) PIK (218)(216) S0296P1 KRRALAAPAA EAREKMLAAKSADGSA MIWLRHRKPELER  1:225-1:1350EEKEEAR PAGE (217) PIK (218) (216) S0297 KPNKALKVKK KRVTQKEELERQRVELRLELDALRSKYE 1:333-1:800 EAGE (219) QQEVEK (220) (221) S0301 KMHTDGRSCLKKGFKLLTDEKSCQDV KRTEKRLRKAIRTL 1:3500-1:5400 EREDTVLEVT DE (223)RKAVHRE (224) E (222) S0303 RVEGPQTESK EETKSTETETGSRVGKL N/A 1:300NEASSRD PE (226) (225) S0305 DKGYLTKEDL KDPLAVDKIMKDLDQC N/A 1:8300-1:10000 RVLMEKE RDGK (228) (227) S0311 EEDLKEVLRSMSRRTRCEDLDELHYQ RRSPIKKVRKSLAL  1:750-1:5000 EAGIELIIEDDI DTDSD (230)DIVDED (231) R (229) S0312 EDYKNTAEWL DERFGDRFPAMSDAYD KVIMDYESLEKAN1:1000-1:3600 LSHTKHR RTMRQR (233) HEE (234) (232) S0314 DQDRKSRLMKGVIVDKDFSHPQMPK RMILKIDDIRKPGE  1:6000-1:30000 GLEALKSHIMA KVED (236)SEE (237) AK (235) S0315 RLQPEFKPKQ KFMQASEDLLKEHYVD DSVESAEKEIGLW 1:9000-1:18000 1_18_2_4_16_315_43 2_26_8_3_206_315_36 LEGTMANCER LKDR(239) FHPEELVD (240) (238) S0316 KSPPESENKE RDGRKVTVIERDLKEPDDHLKEPFLEATDN  1:1000-1:10000 1_25_12_1_85_316_12 2_26_3_3_75_316_31QLEARRRR R (242) SHLR (243) (241) S0319 DLEVKDWMQ EYHKVHQMMREQSILSRHQLLCFKEDCQA 1:900 2_26_6_6_203_319_79 KKRRGLRNSR PSPYEGYR (245)VFQDLEGVEK (244) (246) S0326 GPDILRTYSGA CSLGLALRRWRP (248) N/A 1:120-1:1200 2_25_8_1_42_326_8 3_32_12_4_115_326_45 FVCLE (247) S0330RYLTLDIFAGP N/A N/A  1:2500-1:75000 4_27_11_1_140_330_113_32_7_2_78_330_22 PNYPFSDEY (249) S0330x1 RYLTLDIFAGP N/A N/A 1:600PNYPFSDEY (249) S0331 HYFNSDSFAS N/A N/A 1:300-1:320 2_26_5_5_59_331_29HPNYPYSDEY (250) S0331x1 HYFNSDSFAS N/A N/A 1:150 HPNYPYSDEY (250) S0332RYVVMDFLMD N/A N/A 1:400 2_26_5_5_60_332_61 HPDYPFSDEY (251) S0332x1RYVVMDFLMD N/A N/A 1:100-1:150 HPDYPFSDEY (251) S0336 DPAKVQSLVDRETIPAKLVQSTLSDLR N/A  1:1600 TIREDPD (252) (253) S0342 SDTTEELTVIK N/AN/A  1:400-1:1250 3_25_12_8_111_342_101 3_26_8_3_132_342_36 SSLKDE (254)S0343 HSRSSLMPLR N/A N/A  1:50-1:125 2_18_13_6_30_343_78 NDVDKR (255)S0374 DANTCGEDKG N/A N/A 1:5000-1:9000 2_25_2_4_109_374_55 SRRKFLDGDE(256) S0380 QLEEMTELES N/A N/A 1:2000-1:9000 2_26_8_7_195_380_92PKCKRQENEQ (257) S0384 QADGAASAPT N/A N/A 1:100 EEEEEVVKDR (258) S0388SGDSLETKED N/A N/A 1:600 QKMSPKATEE (259) S0398 KIRLPEREKPD N/A N/A1:200 RERNARREP (260) S0401 RGTKCLRREA N/A N/A  1:600-1:2000 PRWDAPLRDP(261) S0404 GTRSRSHTSE N/A N/A 1:100-1:900 4_18_8_4_9_404_532_26_5_5_66_404_61 4_32_8_6_91_404_77 GTRSRSHTSE (262) S0411 EETTADGRKTN/A N/A  1:1800 QTVCNFTD (263) S0413 AKRKRSAPEK N/A N/A  1:2700 SSGDVP(264) S0414 RVDRPGSRYD N/A N/A 1:100 VSRLGRGKRS (265) S0415 ETVDKLLKGYN/A N/A 1:600-1:1800 DIRLRPD (266) S0417 APGGAEDLED N/A N/A  1:9000TQFPSEEARE (267) S0425 RKSSRTLKKG N/A N/A  1:9000 PRQDPSAIVE (268) S0429GSESGDSDES  1:1200 ESKSEQRTKR (269) S0432 EADSGDARRL N/A N/A  1:90-1:1002_25_11_6_50_432_74 4_26_1_3_209_432_29 2_32_7_1_209_432_7 PRARGERRR H(270) S0440 RLERPQDRDT N/A N/A  1:350-1:1200 1_18_11_6_249_440_56PVQNKRRRS (271) S0445 DRVEDVMME N/A N/A 1:600 RESQFKEKQE (272) S0447DEAFKRLQGK N/A N/A  1:6000 RNRGREE (273) S0455 RFQEEIKENTK N/A N/A 1:900NDKQ (274) S0459 KRDKEGVRW N/A N/A 1:2700-1:8100 TKCNKKTLTD (275) S0469KEGSLLSKQE 1:600 ESKAAFGEE (276) S0494 ESDAGKEKLP N/A N/A  1:2000KMRLPTRSD (277) S0501 ERDKGKTVEV N/A N/A   1:15000 GRAYFETEK (278) S0502EKFRVEKDKL N/A N/A  1:9000 VPEKR (279) S0507 ENYEDDDLVN N/A N/A  1:9000SDEVMKKP (280) S0511 PKADEIRTLVK N/A N/A  1:2000 DMWDTR (281) S0524RKRCLEDSED N/A N/A  1:4500 FGVKKARTE (282) S0527 EPKSFLCRLC N/A N/A 1:1500 CQEDPELDS (283) S0528 EEYDRESKSS N/A N/A  1:350-1:1200 DDVDYRGS(284) S0544 EQRARWERK N/A N/A 1:40  RACTARE (285) S0545 ERKQLECEQV N/AN/A  1:900-1:5400 LQKLAKE (286) S0546 RNSETKVRRS N/A N/A  1:1200TRLQKDLEN (287) S0553 SDYQVISDRQ N/A N/A  1:5400 TPKKDE (288) S0557DIEGKLPQTE N/A N/A 1:200 QELKE (289) S0564 DDVNYKMHFR N/A N/A1:1000-1:8000 3_25_5_3_45_564_33 MINEQQVED (290) S0565 ENPLPERPRE N/AN/A  1:10-1:100 2_18_15_5_99_565_75 KEEPVVR (291) S0567 REQSEGVGA N/AN/A 1:240 RVRRSIGRPE (292) S0578 PRAVAGKEEE  1:1500 DSDPEKALR (293)S0579 EKADTDMEGS N/A N/A 1:400 VDTRQEK (294) S0581 RVQNHDNPK N/A N/A 1:4000-1:10000 WEAKKENISK (295) S0585 RSPPAKGATG N/A N/A 1:500PEEQSDSLK (296) S0586 REEDFKATEII N/A N/A 1:333-1:400 EPSKQDKP (297)S0593 DKTCMKWST N/A N/A  1:500-1:2400 NSCGAQ (298) S0597 DANLSKNGGL N/AN/A  1:3000 EVWL (299) S0604 EPFLPKLLTK N/A N/A  1:2400 (300) S0607RKSEAGSGAA N/A N/A  1:1800 SSSGEDKEN (301) S0609 DDIYDPTYKD N/A N/A1:2000-1:5000 KEGPSPKVE (302) S0611 QSDEAKNNM N/A N/A 1:100 KGLPELEKKD(303) S0612 SRPQGLTEAE N/A N/A  1:4500 QRELEQEAK (304) S0613 RVQQKGTSETN/A N/A 1:250-1:270 DTIC (305) S0614 VRLSPLAERQ N/A N/A 1:1200-1:3000LQVQWE (306) S0616 RRSLGVSVRS N/A N/A  1:2500 WDELPDEKR (307) S0617DEGLGPDPPH N/A N/A 1:600 RDRLRSK (308) S0618 SGKRSSDGSL N/A N/A 1:150SHEEDLAK (309) S0631 SQERSDSPEI N/A N/A 1:600 CHYEKSFHK (310) S0633KVNPEPTHEIR N/A N/A 1:100-1:200 CNSEVK (311) S0645 SDGRGRPAFP N/A N/A1:900 FSCPRQ (312) S0646 GSKEDFDSLL N/A N/A 1:3600-1:5400 QSAKK (313)S0651 QKEEKTWHEA N/A N/A  1:3600 LRSCQADN (314) S0654 EKAEEGPRKR N/A N/A1:400 EPAPLDK (315) S0659 EQNRDRILTPE 1:300 NRK (316) S0663 RDWYIGLVSDN/A N/A 1:900 EKWK (317) S0665 DSYLKTRSPV 1:1500-1:3000 TFLSDLR (318)S0670 KCRGETVAKE N/A N/A 1:900 ISEAMKS (319) S0673 KPPQETEKGH 1:50 SACEPEN (320) S0676 ERRAGSGAR N/A N/A  1:1200 DAERE (321) S0677SEQKADPPAT N/A N/A  1:500-1:1000 EKTLLE (322) S0684 EAEWSQGVQ N/A N/A 1:8100 GTLRIKKYLT (323) S0687 EESKSITEGLL  1:1260 TQKQYE (324) S0691QNFKDAFSGR N/A N/A 1:1575-1:2000 DSSITR (325) S0695 TEDVDEFRNK N/A N/A 1:4050 LQGER (326) S0702 KGDVSNLDPN N/A N/A 1:133650-1:178200FSFEGTKLDV (327)

APPENDIX C ER Pos/ BREAST PROGNOSIS All ER Pos Node Neg ER neg (HHCOHORT) Log rank Log rank Log rank Log rank Dilution Scoring HazardHazard Hazard Hazard AGI ID (1:X) method P value ratio P value ratio Pvalue ratio P value ratio s0021 500 3 0.0001 2.8378 0.0014 3.6147 0.00034.9455 0.0588 2.0089 s0022 100 2 >0.10 0.9363 >0.10 0.6376 >0.100.8219 >0.10 1.4664 s0039 100 1 >0.10 1.1066 0.0625 1.1982 0.05511.2810 >0.10 1.0798 s0040 200 3 0.0389 1.7357 >0.10 1.2649 0.0983 2.23530.0449 2.0825 s0059 300 3 0.0469 1.9686 0.0468 3.7769 0.00995.4146 >0.10 1.3547 s0063 300 2 0.0037 1.7351 0.0418 1.6450 >0.101.5656 >0.10 1.5060 s0068 700 2 0.0218 0.6445 >0.10 0.8501 >0.10 0.77930.0982 0.5632 s0072 6500 2 0.0627 1.4824 0.0069 2.2083 >0.101.0843 >0.10 0.7685 s0073P2 450 2 0.0023 0.5703 >0.10 0.7329 >0.100.6297 0.0987 0.4909 s0076x1 200 2 0.0807 1.2392 >0.10 0.6988 >0.100.9070 >0.10 1.1187 s0079I 400 2 0.0007 1.9503 0.0002 2.5357 >0.101.3644 >0.10 1.1705 s0081 60 2 0.0026 0.5093 0.0384 0.5774 >0.100.8913 >0.10 0.5235 s0137 2500 2 0.0322 1.4856 0.0745 1.5241 0.08721.8527 >0.10 1.2568 s0143P3 630 1 0.0932 1.0806 0.0342 1.1450 >0.101.1183 >0.10 1.0291 s0143P3 630 3 0.0294 1.7362 0.0103 2.1681 >0.101.7919 >0.10 1.2595 s0235 4500 2 0.0174 1.6960 0.0284 1.8866 >0.101.4827 >0.10 1.4227 s0237 1000 3 >0.10 1.3805 >0.10 1.9314 0.04313.2504 >0.10 0.9726 s0255n 1000 2 >0.10 0.7361 0.0933 0.6593 >0.100.6550 >0.10 0.9813 s0260 5400 2 >0.10 1.0896 0.0695 0.2945 >0.100.6016 >0.10 1.2113 s0296P1 225 2 0.0038 1.7491 0.0002 2.5560 0.04662.3419 >0.10 0.8519 s0303 300 2 0.0860 1.4072 >0.10 1.1960 >0.101.5662 >0.10 1.3788 s0305 8332 2 0.0809 1.2267 >0.10 1.1590 >0.100.9719 >0.10 1.2343 s0330x1 600 2 >0.10 0.9730 0.0134 3.3569 0.06325.4988 0.0555 0.0021 s0343 125 2 >0.10 0.7487 0.0795 0.6256 >0.100.5594 >0.10 1.1414 s0398 200 2 0.0125 0.4725 >0.10 0.6070 >0.10 0.88460.0725 0.1956 s0398 200 3 0.0551 0.3428 0.0790 0.3049 >0.10 0.4646 >0.100.6364 s0404 150 1 0.0321 1.1427 >0.10 1.1160 >0.10 1.1811 >0.10 1.0783s0404 150 3 0.0087 1.8696 >0.10 1.7755 0.0727 2.3524 >0.10 1.4714 s04592700 2 >0.10 1.3287 >0.10 1.4538 >0.10 1.2304 >0.10 0.9768 s0545 900 20.0000 2.2547 0.0048 2.1037 >0.10 1.7913 0.0300 2.0266 s0654 400 30.0050 2.8738 >0.10 1.5890 >0.10 1.2356 0.0119 3.1822 s0670 900 2 >0.100.7709 0.0715 0.5411 0.0652 0.2800 >0.10 0.9506 s0676 1200 1 0.00881.1968 >0.10 1.0888 >0.10 1.2110 0.0130 1.2678 s0677 500 2 0.0041 1.71830.0289 1.7290 >0.10 1.1095 >0.10 1.3276 s0691NM 1575 2 0.02801.8399 >0.10 1.0211 >0.10 1.6936 0.0761 1.8725 s0702 178200 2 0.00051.9066 0.0000 2.8624 0.0120 2.6656 >0.10 0.9739 s6001 na 1 0.0292 0.8715na na na na na na s6002 na 1 0.0027 0.8341 0.0214 0.8319 >0.100.8850 >0.10 0.9325 s6003 na 3 0.0176 1.9477 0.0051 3.1766 >0.101.3355 >0.10 1.2017 s6006 na 2 0.0194 1.5374 0.0958 1.5021 0.01352.3697 >0.10 1.2597 s6007 na 1 0.0756 1.1144 0.0107 1.2055 >0.10 1.07470.0342 0.9164

APPENDIX D LUNG PROGNOSIS All Adenocarcinoma Squamous cell (HH COHORT)Log rank Chi Log rank Chi Log rank Chi Dilution Scoring Hazard squareHazard square Hazard square AGI ID (1:X) method P value ratio P value Pvalue ratio P value P value ratio P value s0021 1500 3 0.1288 1.83200.0240 0.2188 1.6167 0.0860 0.0005 0.0657 0.0100 s0022 250 2 0.01160.3747 0.1000 >0.10 nd >0.10 >0.10 nd >0.10 s0039 400 2 0.3532 1.54690.0080 >0.10 nd >0.10 >0.10 nd >0.10 s0046 300 2 0.0145 0.51650.0270 >0.10 nd >0.10 0.0529 0.3302 0.0850 s0063 1200 2 >0.10 nd >0.100.0831 1.8631 0.0550 >0.10 nd >0.10 s0072 6500 2 0.2633 1.35700.0680 >0.10 nd >0.10 >0.10 nd >0.10 s0073P2 50 2 0.0935 0.46400.0430 >0.10 nd >0.10 >0.10 nd >0.10 s0074P3 810 3 0.0723 0.00220.0530 >0.10 nd >0.10 >0.10 nd >0.10 s0137 5000 2 0.0610 1.6429 0.10100.2271 1.5312 0.0660 >0.10 nd >0.10 s0143P3 300 3 >0.10 nd >0.10 0.00084.5211 0.0270 >0.10 nd >0.10 s0296P1 1350 2 0.0783 1.6148 0.0460 0.02372.1849 0.0180 0.1042 0.3968 0.0840 s0303 300 2 >0.10 nd >0.10 0.04692.0494 0.5360 >0.10 nd >0.10 s0330 15000 3 >0.10 nd >0.10 >0.10 nd >0.100.0124 0.2278 0.0460 s0330 45000 3 0.0880 0.5248 0.0440 >0.10 nd >0.100.0188 0.1270 0.0130 s0330x1 600 3 >0.10 nd >0.10 >0.10 nd >0.10 0.04550.1631 0.0080 s0331 300 3 0.2157 0.6603 0.0850 >0.10 nd >0.10 0.04040.2350 0.0050 s0331x1 300 3 >0.10 nd >0.10 >0.10 nd >0.10 0.0705 0.27440.0360 s0332 400 3 0.1496 0.5639 0.0680 >0.10 nd >0.10 0.0621 0.17850.0160 s0332x1 150 2 >0.10 nd >0.10 >0.10 nd >0.10 0.0321 0.1466 0.1290s0398 200 2 0.1253 0.5775 0.0870 >0.10 nd >0.10 0.3348 0.6094 0.0640s0404 900 3 0.1273 1.7817 0.0420 >0.10 nd >0.10 >0.10 nd >0.10 s05452700 2 0.1246 1.8432 0.0150 0.0191 3.2839 0.0180 >0.10 nd >0.10 s0586400 2 0.0204 0.4659 0.4380 0.1322 0.2457 0.0960 >0.10 nd >0.10 s06911575 3 >0.10 nd >0.10 >0.10 nd >0.10 0.0608 3.1820 0.0420 s0702 1782001 >0.10 nd >0.10 >0.10 nd >0.10 0.0463 0.6944 0.5360 s6006 1 2 >0.10nd >0.10 0.1259 1.7720 0.0550 >0.10 nd >0.10 s6007 1 2 >0.10 nd >0.100.0316 3.4266 0.0110 >0.10 nd >0.10 s6008 1 2 >0.10 nd >0.10 >0.10nd >0.10 0.2388 1312.0118 0.0230 s6013 1 2 >0.10 nd >0.10 0.0154 2.57550.0540 >0.10 nd >0.10 s0614 3000 2 0.0930 1.5785 0.0860 >0.10nd >0.10 >0.10 nd >0.10

APPENDIX E ER Pos/ BREAST PROGNOSIS All ER Pos Node Neg ER neg (HHCOHORT) Log rank Log rank Log rank Log rank Dilution Scoring HazardHazard Hazard Hazard AGI ID (1:X) method P value ratio P value ratio Pvalue ratio P value ratio s0022 100 2 >0.10 0.8660 0.0633 0.5679 >0.100.8810 >0.10 1.3696 s0053 30 2 0.0496 0.6276 >0.10 0.7265 >0.101.0372 >0.10 0.5002 s0059P2 90 2 0.0088 1.9934 >0.10 1.7964 >0.101.8087 >0.10 1.4654 s0063 300 2 0.0031 1.7208 0.0955 1.5202 >0.101.5656 >0.10 1.7249 s0063 600 2 0.0006 1.9517 0.0154 2.0148 0.06602.4507 >0.10 1.4408 s0068 700 2 0.0312 0.6537 >0.10 0.8644 >0.10 0.77930.0936 0.5597 s0072 6500 2 >0.10 1.4087 0.0307 2.0193 >0.10 1.3344 >0.100.7426 s0073p2 450 2 0.0060 0.6051 >0.10 0.8755 >0.10 1.0495 >0.100.4614 s0076x1 200 2 0.0471 1.6431 >0.10 0.5060 >0.10 0.8227 >0.101.3384 s0079 400 2 0.0006 1.8382 0.0050 2.0469 >0.10 1.0820 >0.10 1.2420s0081 60 2 0.0006 0.5518 0.0621 0.6335 >0.10 0.7897 >0.10 0.4758 s0140500 2 0.0146 1.7628 >0.10 1.7553 >0.10 0.6881 >0.10 1.3817 s0235 4500 20.0002 1.9769 0.0017 2.1999 >0.10 1.5655 >0.10 1.5469 s0253 2000 20.0579 1.5740 >0.10 1.5128 0.0784 2.1386 >0.10 1.4763 s0253 500 2 >0.101.1127 >0.10 0.7030 >0.10 0.8017 0.0377 2.1389 s0255 1000 2 0.06100.6995 0.0603 0.6193 >0.10 0.6550 >0.10 0.8992 s0296P1 225 1 0.00281.1896 0.0001 1.3344 0.0008 1.3554 >0.10 0.9879 s0296P1 225 2 0.00521.7183 0.0014 2.3378 0.0466 2.3419 >0.10 0.8994 s0305 8332 2 0.09451.5035 >0.10 1.2945 >0.10 0.9447 >0.10 1.4199 s0330x1 600 2 >0.10 0.74560.0945 2.6186 0.0632 5.4988 0.0525 0.0021 s0404 150 2 0.02651.6475 >0.10 1.3534 >0.10 1.8109 >0.10 1.4264 s0404 150 3 0.0027 2.07190.0706 1.8942 0.0623 2.4347 >0.10 1.6912 s0440 1200 2 0.05810.6270 >0.10 0.8110 >0.10 0.6788 >0.10 0.2904 s0545 2700 2 0.00002.4278 >0.10 1.7002 >0.10 0.7788 0.0024 2.4284 s0545 900 2 0.0000 2.24470.0055 2.1160 >0.10 1.7913 0.0412 1.9465 s0654 400 2 >0.10 0.8150 0.06760.4627 >0.10 0.6037 >0.10 1.7718 s0670 900 2 >0.10 0.7040 0.0258 0.43790.0652 0.2800 >0.10 0.8549 s0676 1200 1 0.0113 1.1614 >0.10 1.0623 >0.101.2110 0.0212 1.2442 s0676 1200 2 0.0392 1.5231 >0.10 1.2602 >0.101.9314 0.0875 1.8335 s0677 1000 1 0.0017 1.1649 0.0076 1.2173 >0.101.0408 >0.10 1.0892 s0677 1000 2 0.0123 1.5683 0.0148 1.8170 >0.101.0567 >0.10 1.2487 s0687 1260 2 0.0830 0.6973 0.0519 0.5427 >0.101.1017 >0.10 0.8919 s0691 1575 1 0.0001 1.2824 >0.10 1.0463 >0.10 1.12250.0058 1.2902 s0691 1575 2 0.0020 2.1106 >0.10 1.1418 >0.10 1.69360.0217 2.1925 s0695 2700 2 0.0459 1.4465 >0.10 1.0295 >0.10 0.9663 >0.101.2102 s0702 178200 2 0.0001 2.0291 0.0000 3.1207 0.0010 3.3919 >0.101.0431 s6001 na 2 0.0009 0.5721 >0.10 1.0000 >0.10 1.0000 >0.10 1.0000s6002 na 2 0.0004 0.5236 0.0083 0.5255 >0.10 0.6236 >0.10 0.9192 s6006na 1 0.0413 1.1246 0.0591 1.1129 0.0334 1.3081 >0.10 1.0566 s6006 na 20.0399 1.4388 >0.10 1.3185 0.0095 2.4996 >0.10 1.2815 s6007 na 1 >0.101.1140 0.0086 1.1848 >0.10 1.0585 0.0284 0.9403 s6007 na 2 0.0358 1.48030.0537 1.6016 >0.10 1.0875 >0.10 0.9724 s6007 na 3 >0.10 1.1979 0.00322.4860 >0.10 2.1836 0.0232 0.4636

APPENDIX F LUNG PROGNOSIS HH 5 yr UAB (HH & UAB COHORTS) recurrence 5 yrsurvival Dilution Scoring Hazard Hazard AGI ID (1:X) method P valueratio P value ratio s0073P2 50 2 0.129 0.497 0.192 0.660 All s0074P3 8103 0.091 0.002 0.096 0.002 s0586 400 2 0.007 0.385 0.148 0.619 s6007 1 20.081 2.420 0.112 2.099 s0074P3 810 3 0.166 0.002 0.076 0.002Adenocarcinoma s0143P3 300 3 0.001 4.521 0.023 4.450 s0296P1 1350 20.024 2.185 0.021 2.214 s0303 300 2 0.047 2.049 0.007 3.358 s6006 1 20.126 1.772 0.164 1.650 s6007 1 2 0.032 3.427 0.033 2.734

We claim:
 1. A kit comprising a prognostic panel of antibodies thatincludes: a first antibody that binds to SLC7A5; and a second antibodythat binds to a biomarker selected from the group consisting of p53,NDRG1, HTF9C, and CEACAM5.
 2. The kit of claim 1, wherein the secondantibody binds to HTF9C.
 3. The kit of claim 1, wherein the kit is forprognosis of a breast cancer patient.
 4. The kit of claim 3, wherein thebreast cancer is estrogen receptor positive (ER+).
 5. The kit of claim4, wherein the breast cancer is node negative (Node−).
 6. The kit ofclaim 3, wherein across a population of breast cancer patients, a higherlevel of binding of each of the antibodies correlates with a higherlikelihood that the patient will die from breast cancer or have arecurrence.
 7. A kit comprising a prognostic panel of antibodies thatincludes: a first antibody that binds to HTF9C; and a second antibodythat binds to a biomarker selected from the group consisting of SLC7A5,p53, NDRG1, and CEACAM5.
 8. The kit of claim 7, wherein the kit is forprognosis of a breast cancer patient.
 9. The kit of claim 8, wherein thebreast cancer is estrogen receptor positive (ER+).
 10. The kit of claim9, wherein the breast cancer is node negative (Node−).
 11. The kit ofclaim 8, wherein across a population of breast cancer patients, a higherlevel of binding of each of the antibodies correlates with a higherlikelihood that the patient will die from breast cancer or have arecurrence.
 12. A kit comprising a prognostic panel of antibodies thatincludes: a first antibody that binds to SLC7A5; a second antibody thatbinds to HTF9C; and a third antibody that binds to a biomarker selectedfrom the group consisting of p53, NDRG1, and CEACAM5.
 13. The kit ofclaim 12, wherein the kit is for prognosis of a breast cancer patient.14. The kit of claim 13, wherein the breast cancer is estrogen receptorpositive (ER+).
 15. The kit of claim 14, wherein the breast cancer isnode negative (Node−).
 16. The kit of claim 13, wherein across apopulation of breast cancer patients, a higher level of binding of eachof the antibodies correlates with a higher likelihood that the patientwill die from breast cancer or have a recurrence.
 17. A kit comprising aprognostic panel of antibodies that includes: a first antibody thatbinds to SLC7A5; a second antibody that binds to HTF9C; a third antibodythat binds to p53; a fourth antibody that binds to NDRG1; and a fifthantibody that binds to CEACAM5.
 18. The kit of claim 17, wherein the kitis for prognosis of a breast cancer patient.
 19. The kit of claim 18,wherein the breast cancer is estrogen receptor positive (ER+).
 20. Thekit of claim 19, wherein the breast cancer is node negative (Node−). 21.The kit of claim 18, wherein across a population of breast cancerpatients, a higher level of binding of each of the antibodies correlateswith a higher likelihood that the patient will die from breast cancer orhave a recurrence.
 22. A kit comprising a prognostic panel ofantibodies, the prognostic panel of antibodies consisting essentiallyof: a first antibody that binds to SLC7A5; a second antibody that bindsto HTF9C; and one, two, or three antibodies that each bind to adifferent biomarker selected from the group consisting of p53, NDRG1,and CEACAM5.
 23. The kit of claim 22, wherein the kit is for prognosisof a breast cancer patient.
 24. The kit of claim 23, wherein the breastcancer is estrogen receptor positive (ER+).
 25. The kit of claim 24,wherein the breast cancer is node negative (Node−).
 26. The kit of claim23, wherein across a population of breast cancer patients, a higherlevel of binding of each of the antibodies correlates with a higherlikelihood that the patient will die from breast cancer or have arecurrence.
 27. A kit comprising a prognostic panel of antibodies, theprognostic panel of antibodies consisting essentially of: a firstantibody that binds to SLC7A5; a second antibody that binds to HTF9C; athird antibody that binds to p53; a fourth antibody that binds to NDRG1;and a fifth antibody that binds to CEACAM5.
 28. The kit of claim 27,wherein the kit is for prognosis of a breast cancer patient.
 29. The kitof claim 28, wherein the breast cancer is estrogen receptor positive(ER+).
 30. The kit of claim 29, wherein the breast cancer is nodenegative (Node−).
 31. The kit of claim 28, wherein across a populationof breast cancer patients, a higher level of binding of each of theantibodies correlates with a higher likelihood that the patient will diefrom breast cancer or have a recurrence.